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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "CODE TO EVALUATE THE PERFORMANCES OF THE LPD ALGORITHM ON miniPET DATA"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np \n",
      "import odl \n",
      "import odlpet \n",
      "import torch \n",
      "from torch import nn\n",
      "from torch.nn import functional as F\n",
      "from torch import optim \n",
      "import torchvision\n",
      "from torchvision import datasets, transforms \n",
      "import matplotlib.pyplot as plt \n",
      "from odlpet.scanner.scanner import Scanner \n",
      "from odlpet.scanner.compression import Compression\n",
      "from odl.contrib.torch import OperatorAsModule \n",
      "import time \n",
      "from odl.contrib import fom\n",
      "import warnings\n",
      "import GPUtil\n",
      "from torch.utils.data.dataset import Dataset, TensorDataset\n",
      "import nibabel\n",
      "import re\n",
      "import glob\n",
      "\n",
      "warnings.filterwarnings('ignore')\n",
      "GPUtil.showUtilization()\n",
      "torch.manual_seed(123)\n",
      "torch.cuda.set_device(0)\n",
      "torch.cuda.empty_cache()\n",
      "GPUtil.showUtilization()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "| ID | GPU | MEM |\n",
        "------------------\n",
        "|  0 |  0% |  0% |\n",
        "|  1 |  0% |  0% |\n",
        "|  2 |  0% |  0% |\n",
        "|  3 |  0% |  0% |\n",
        "|  4 |  0% |  0% |\n",
        "|  5 |  0% |  0% |\n",
        "|  6 |  0% |  0% |\n",
        "|  7 |  0% |  0% |\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "| ID | GPU | MEM |\n",
        "------------------\n",
        "|  0 |  0% |  0% |\n",
        "|  1 |  0% |  0% |\n",
        "|  2 |  0% |  0% |\n",
        "|  3 |  0% |  0% |\n",
        "|  4 |  0% |  0% |\n",
        "|  5 |  0% |  0% |\n",
        "|  6 |  0% |  0% |\n",
        "|  7 |  0% |  0% |\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# PROJECTOR DEFINITION\n",
      "\n",
      "scanner = Scanner() \n",
      "scanner.num_rings = 1\n",
      "compression = Compression(scanner) \n",
      "compression.max_num_segments = 0 \n",
      "compression.num_of_views = 180 \n",
      "compression.num_non_arccor_bins = 147 \n",
      "compression.data_arc_corrected = True\n",
      "pet_projector = compression.get_projector(restrict_to_cylindrical_FOV=False)\n",
      "pet_projector_adj=pet_projector.adjoint\n",
      "pet_projector.range\n",
      "pet_projector.domain \n",
      "fwd_op_mod=OperatorAsModule(pet_projector)\n",
      "fwd_op_adj_mod = OperatorAsModule(pet_projector_adj)\n",
      "normalisation=(pet_projector.norm(estimate=True))**2\n",
      "transformations = transforms.Compose([transforms.ToTensor()])\n",
      "\n",
      "# MLEM RECONSTRUCTION OPERATORS DEFINITIONS\n",
      "\n",
      "class MLEM(odl.operator.Operator):\n",
      "    def __init__(self, op, niter):\n",
      "        super(MLEM, self).__init__(domain=pet_projector.range, range=pet_projector.domain, linear=True) \n",
      "        self.op = op \n",
      "        self.niter = niter \n",
      "    \n",
      "    def _call(self, data):\n",
      "        reco = self.range.one()\n",
      "        odl.solvers.iterative.statistical.mlem(self.op, reco, data, niter=self.niter) \n",
      "        return reco\n",
      "    \n",
      "mlem_op_comp=MLEM(pet_projector,niter=20) \n",
      "mlem_op_comp_mod=OperatorAsModule(mlem_op_comp) \n",
      "\n",
      "# FUNCTION TO COMPUTE PSNR\n",
      "\n",
      "def peak_SNR(img,ground_truth):\n",
      "    \n",
      "    img=torch.tensor(img/np.amax(img.flatten()))\n",
      "    ground_truth=torch.tensor(ground_truth/np.amax(ground_truth.flatten()))\n",
      "    \n",
      "    mse=nn.MSELoss() \n",
      "    return -10*np.log10(mse(img.float(),ground_truth.float()))\n",
      "\n",
      "# FUNCTION TO LOAD miniPET DATA\n",
      "\n",
      "def dataLoad(filename):\n",
      "    scanner2 = Scanner() \n",
      "    scanner2.num_rings = 35 \n",
      "    compression2 = Compression(scanner2) \n",
      "    compression2.max_diff_ring = 0\n",
      "    compression2.max_num_segments = 0 \n",
      "    compression2.num_of_views = 180 \n",
      "    compression2.num_non_arccor_bins = 147 \n",
      "    compression2.data_arc_corrected = True\n",
      "    pet_projector2 = compression2.get_projector(restrict_to_cylindrical_FOV=False) \n",
      "    pet_projector_adj2=pet_projector2.adjoint\n",
      "    real=nibabel.load(filename) \n",
      "    real_data = real.get_data() \n",
      "    real_data_ = np.transpose(real_data, (0,2,1)) \n",
      "    data = pet_projector2.range.element(real_data_) \n",
      "    data[data.asarray() < 0] = 0 \n",
      "    a = np.zeros((1,1,data.shape[0],data.shape[1],data.shape[2]))\n",
      "    a[0,0,:,:,:]=data\n",
      "    data=torch.tensor(a)\n",
      "    data=volumeSplitter(data)\n",
      "    return data\n",
      "\n",
      "# FUNCTION TO SPLIT A 3D VOLUME INTO 2D SLICES \n",
      "\n",
      "def volumeSplitter(inputTensor):\n",
      "\n",
      "    \n",
      "    volumes = inputTensor.numpy()\n",
      "    newVolumes = np.zeros((volumes.shape[2],1,volumes.shape[3],volumes.shape[4]))\n",
      "\n",
      "    i = 0\n",
      "    while i < volumes.shape[0]:\n",
      "            \n",
      "        k = 0\n",
      "        while  k < volumes.shape[2]:\n",
      "            newVolumes[k+i*volumes.shape[2],0,:,:] = volumes[i,0,k,:,:]\n",
      "            k = k+1\n",
      "                \n",
      "        i = i+1\n",
      "            \n",
      "    newVolumes = torch.tensor(newVolumes)\n",
      "    return newVolumes\n",
      "\n",
      "# FUNCTION TO MERGE 2D SLICES INTO A 3D VOLUME\n",
      "\n",
      "def volumeMerger(inputTensor):        \n",
      "\n",
      "    volumes = inputTensor\n",
      "    reduced = np.ceil(volumes.shape[0]/35)\n",
      "    newVolumes = np.zeros((reduced.astype(int),1,35,volumes.shape[2],volumes.shape[3]))\n",
      "\n",
      "    i = 0\n",
      "    while i < newVolumes.shape[0]:\n",
      "            \n",
      "        k = 0\n",
      "        while  k < 35:\n",
      "            newVolumes[i,0,k,:,:] = volumes[k+i*35,0,:,:]\n",
      "            k = k+1\n",
      "                \n",
      "        i = i+1\n",
      "        \n",
      "    return newVolumes\n",
      "\n",
      "# FUNCTION TO LOAD THE TARGET IMAGE\n",
      "\n",
      "def GTprocessing(filename,uniform):\n",
      "    from scipy.ndimage import zoom\n",
      "    gt_image_in=nibabel.load(filename)\n",
      "    gt_image = gt_image_in.get_data()/1000 \n",
      "    gt_image = np.transpose(gt_image, (0,2,1)) \n",
      "    gt_image = np.flip(gt_image,1)\n",
      "    gt_image = zoom(gt_image,(1,0.395,0.395))\n",
      "    temp = np.zeros((gt_image.shape[0],1,gt_image.shape[1],gt_image.shape[2]))\n",
      "    temp[:,0,:,:]=gt_image\n",
      "    gt_image = torch.tensor(temp)\n",
      "    if uniform:\n",
      "        top = 0.4\n",
      "        bot = 0.1\n",
      "        gt_image[gt_image<bot]=0\n",
      "        gt_image[np.logical_and(gt_image<top,gt_image>bot)]=torch.mean(gt_image[np.logical_and(gt_image<top,gt_image>bot)])\n",
      "    return gt_image"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# DUAL NETS ARCHITECTURE DEFINITION\n",
      "\n",
      "class UnetD(nn.Module):\n",
      "    \n",
      "    def __init__(self,n):\n",
      "        \n",
      "        super(UnetD,self).__init__()\n",
      "        \n",
      "        self.layerb = nn.Sequential(\n",
      "                        nn.Conv2d(n,32,3,padding=1),\n",
      "                        nn.BatchNorm2d(32),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        nn.Conv2d(32,32,3,stride = 1,padding=1),\n",
      "                        nn.BatchNorm2d(32),\n",
      "                        nn.ReLU(inplace=True) \n",
      "        )\n",
      "        \n",
      "        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
      "        \n",
      "        self.layer2 = nn.Sequential(\n",
      "                        nn.Conv2d(32,64,3,padding=1),\n",
      "                        nn.BatchNorm2d(64),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        nn.Conv2d(64,64,3,stride = 1,padding=1),\n",
      "                        nn.BatchNorm2d(64),\n",
      "                        nn.ReLU(inplace=True)\n",
      "        )\n",
      "        \n",
      "        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
      "        \n",
      "        self.layer3 = nn.Sequential(\n",
      "                        nn.Conv2d(64,128,3,padding=1),\n",
      "                        nn.BatchNorm2d(128),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        nn.Conv2d(128,128,3,stride = 1,padding=1),\n",
      "                        nn.BatchNorm2d(128),\n",
      "                        nn.ReLU(inplace=True)\n",
      "        )\n",
      "        \n",
      "        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
      "        \n",
      "        self.layer4 = nn.Sequential(\n",
      "                        nn.Conv2d(128,256,3,padding=1),\n",
      "                        nn.BatchNorm2d(256),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        nn.Conv2d(256,256,3,stride = 1,padding=1),\n",
      "                        nn.BatchNorm2d(256),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        \n",
      "        )\n",
      "        \n",
      "        \n",
      "        self.layerUP31 = nn.Sequential(\n",
      "                        nn.ConvTranspose2d(256,128,3,2,(0,1),(0,1)),\n",
      "                        nn.BatchNorm2d(128),\n",
      "                        nn.ReLU(inplace=True)\n",
      "                        \n",
      "        )\n",
      "        \n",
      "        self.layerUP32 = nn.Sequential(\n",
      "                        nn.Conv2d(256,128,3,1,1),\n",
      "                        nn.BatchNorm2d(128),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        nn.Conv2d(128,128,3,1,1),\n",
      "                        nn.BatchNorm2d(128),\n",
      "                        nn.ReLU(inplace=True)\n",
      "                        \n",
      "        )\n",
      "        \n",
      "        self.layerUP41 = nn.Sequential(\n",
      "                        nn.ConvTranspose2d(128,64,3,2,(1,0),(1,0)),\n",
      "                        nn.BatchNorm2d(64),\n",
      "                        nn.ReLU(inplace=True)\n",
      "        )\n",
      "        \n",
      "        self.layerUP42 = nn.Sequential(\n",
      "                        nn.Conv2d(128,64,3,1,1),\n",
      "                        nn.BatchNorm2d(64),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        nn.Conv2d(64,64,3,1,1),\n",
      "                        nn.BatchNorm2d(64),\n",
      "                        nn.ReLU(inplace=True)\n",
      "        )\n",
      "        \n",
      "        self.layerUP51 = nn.Sequential(\n",
      "                        nn.ConvTranspose2d(64,32,3,2,(1,0),(1,0)),\n",
      "                        nn.BatchNorm2d(32),\n",
      "                        nn.ReLU(inplace=True)\n",
      "        )\n",
      "        self.layerUP52 = nn.Sequential(\n",
      "                        nn.Conv2d(64,32,3,1,1),\n",
      "                        nn.BatchNorm2d(32),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        nn.Conv2d(32,32,3,1,1),\n",
      "                        nn.BatchNorm2d(32),\n",
      "                        nn.ReLU(inplace=True)\n",
      "        )\n",
      "        \n",
      "        self.layerUP61 = nn.Sequential(\n",
      "                        nn.Conv2d(32,1,1,1,0)\n",
      "        )\n",
      "        \n",
      "          \n",
      "    def forward(self,x):\n",
      "        \n",
      "        #print(x.shape)\n",
      "        l1 = self.layerb(x)\n",
      "        #print(l1.shape)\n",
      "        l2 = self.layer2(self.pool1(l1))\n",
      "        #print(l2.shape)\n",
      "        l3 = self.layer3(self.pool2(l2))\n",
      "        #print(l3.shape)\n",
      "\n",
      "        mid = self.layer4(self.pool3(l3))\n",
      "        #print(mid.shape)\n",
      "        \n",
      "        l3up = self.layerUP31(mid)\n",
      "        l3up = torch.cat((l3up,l3),dim=1)\n",
      "        l3up = self.layerUP32(l3up)\n",
      "        #print(l3up.shape)\n",
      "        l4up = self.layerUP41(l3up)\n",
      "        l4up = torch.cat((l4up,l2),dim=1)\n",
      "        l4up = self.layerUP42(l4up)\n",
      "        #print(l4up.shape)\n",
      "        l5up = self.layerUP51(l4up)\n",
      "        l5up = torch.cat((l5up,l1),dim=1)\n",
      "        l5up = self.layerUP52(l5up)\n",
      "        #print(l5up.shape)\n",
      "        out = self.layerUP61(l5up)\n",
      "        #print(out.shape)\n",
      "        \n",
      "        return out\n",
      "\n",
      "\n",
      "Unet1 = UnetD(1).cuda()\n",
      "Unet3 = UnetD(3).cuda()\n",
      "Unet5 = UnetD(4).cuda()\n",
      "\n",
      "# PRIMAL NETS ARCHITECTURE DEFINITION\n",
      "\n",
      "class UnetP(nn.Module):\n",
      "    \n",
      "    def __init__(self,n):\n",
      "        \n",
      "        super(UnetP,self).__init__()\n",
      "        \n",
      "        self.layerb = nn.Sequential(\n",
      "                        nn.Conv2d(n,32,3,padding=1),\n",
      "                        nn.BatchNorm2d(32),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        nn.Conv2d(32,32,3,stride = 1,padding=1),\n",
      "                        nn.BatchNorm2d(32),\n",
      "                        nn.ReLU(inplace=True) \n",
      "        )\n",
      "        \n",
      "        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
      "        \n",
      "        self.layer2 = nn.Sequential(\n",
      "                        nn.Conv2d(32,64,3,padding=1),\n",
      "                        nn.BatchNorm2d(64),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        nn.Conv2d(64,64,3,stride = 1,padding=1),\n",
      "                        nn.BatchNorm2d(64),\n",
      "                        nn.ReLU(inplace=True)\n",
      "        )\n",
      "        \n",
      "        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
      "        \n",
      "        self.layer3 = nn.Sequential(\n",
      "                        nn.Conv2d(64,128,3,padding=1),\n",
      "                        nn.BatchNorm2d(128),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        nn.Conv2d(128,128,3,stride = 1,padding=1),\n",
      "                        nn.BatchNorm2d(128),\n",
      "                        nn.ReLU(inplace=True)\n",
      "        )\n",
      "        \n",
      "        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
      "        \n",
      "        self.layer4 = nn.Sequential(\n",
      "                        nn.Conv2d(128,256,3,padding=1),\n",
      "                        nn.BatchNorm2d(256),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        nn.Conv2d(256,256,3,stride = 1,padding=1),\n",
      "                        nn.BatchNorm2d(256),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        \n",
      "        )\n",
      "        \n",
      "        \n",
      "        self.layerUP31 = nn.Sequential(\n",
      "                        nn.ConvTranspose2d(256,128,3,2,1,1),\n",
      "                        nn.BatchNorm2d(128),\n",
      "                        nn.ReLU(inplace=True)\n",
      "                        \n",
      "        )\n",
      "        \n",
      "        self.layerUP32 = nn.Sequential(\n",
      "                        nn.Conv2d(256,128,3,1,1),\n",
      "                        nn.BatchNorm2d(128),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        nn.Conv2d(128,128,3,1,1),\n",
      "                        nn.BatchNorm2d(128),\n",
      "                        nn.ReLU(inplace=True)\n",
      "                        \n",
      "        )\n",
      "        \n",
      "        self.layerUP41 = nn.Sequential(\n",
      "                        nn.ConvTranspose2d(128,64,3,2,0,0),\n",
      "                        nn.BatchNorm2d(64),\n",
      "                        nn.ReLU(inplace=True)\n",
      "        )\n",
      "        \n",
      "        self.layerUP42 = nn.Sequential(\n",
      "                        nn.Conv2d(128,64,3,1,1),\n",
      "                        nn.BatchNorm2d(64),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        nn.Conv2d(64,64,3,1,1),\n",
      "                        nn.BatchNorm2d(64),\n",
      "                        nn.ReLU(inplace=True)\n",
      "        )\n",
      "        \n",
      "        self.layerUP51 = nn.Sequential(\n",
      "                        nn.ConvTranspose2d(64,32,3,2,0,0),\n",
      "                        nn.BatchNorm2d(32),\n",
      "                        nn.ReLU(inplace=True)\n",
      "        )\n",
      "        self.layerUP52 = nn.Sequential(\n",
      "                        nn.Conv2d(64,32,3,1,1),\n",
      "                        nn.BatchNorm2d(32),\n",
      "                        nn.ReLU(inplace=True),\n",
      "                        nn.Conv2d(32,32,3,1,1),\n",
      "                        nn.BatchNorm2d(32),\n",
      "                        nn.ReLU(inplace=True)\n",
      "        )\n",
      "        \n",
      "        self.layerUP61 = nn.Sequential(\n",
      "                        nn.Conv2d(32,1,1,1,0)\n",
      "        )\n",
      "        \n",
      "          \n",
      "    def forward(self,x):\n",
      "        \n",
      "        #print(x.shape)\n",
      "        l1 = self.layerb(x)\n",
      "        #print(l12.shape)\n",
      "        l2 = self.layer2(self.pool1(l1))\n",
      "        #print(l2.shape)\n",
      "        l3 = self.layer3(self.pool2(l2))\n",
      "        #print(l3.shape)\n",
      "\n",
      "        mid = self.layer4(self.pool3(l3))\n",
      "        #print(mid.shape)\n",
      "        \n",
      "        l3up = self.layerUP31(mid)\n",
      "        l3up = torch.cat((l3up,l3),dim=1)\n",
      "        l3up = self.layerUP32(l3up)\n",
      "        #print(l3up.shape)\n",
      "        l4up = self.layerUP41(l3up)\n",
      "        l4up = torch.cat((l4up,l2),dim=1)\n",
      "        l4up = self.layerUP42(l4up)\n",
      "        #print(l4up.shape)\n",
      "        l5up = self.layerUP51(l4up)\n",
      "        l5up = torch.cat((l5up,l1),dim=1)\n",
      "        l5up = self.layerUP52(l5up)\n",
      "        #print(l5up.shape)\n",
      "        out = self.layerUP61(l5up)\n",
      "        #print(out.shape)\n",
      "        \n",
      "        return out\n",
      "\n",
      "Unet2 = UnetP(1).cuda()\n",
      "Unet4 = UnetP(2).cuda()\n",
      "Unet6 = UnetP(3).cuda()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# LPD ALGORITHM DEFINITION\n",
      "\n",
      "def LPD(Unet1,Unet2,Unet3,Unet4,Unet5,Unet6,sino,normalization,see):\n",
      "\n",
      "        sino = sino.cuda().float()\n",
      "        # Iter 0\n",
      "        h0 = Unet1(sino)\n",
      "\n",
      "        h0_img = fwd_op_adj_mod(h0)/normalization\n",
      "\n",
      "        f0 = Unet2(h0_img)\n",
      "\n",
      "        # Iter 1\n",
      "\n",
      "        h0 = h0.cpu().detach().cuda()\n",
      "        f0 = f0.cpu().detach().cuda()\n",
      "\n",
      "        f0_data = fwd_op_mod(f0)\n",
      "\n",
      "        outh1 = Unet3(torch.cat((h0,f0_data,sino),dim=1))\n",
      "\n",
      "        h1 = outh1 + h0\n",
      "\n",
      "        h1_img = fwd_op_adj_mod(h1)/normalization\n",
      "\n",
      "        outf1 = Unet4(torch.cat((f0,h1_img),dim=1))\n",
      "\n",
      "        f1 = outf1 + f0\n",
      "\n",
      "        # Iter 2\n",
      "\n",
      "        h1 = h1.cpu().detach().cuda()\n",
      "        f1 = f1.cpu().detach().cuda()\n",
      "\n",
      "        f1_data = fwd_op_mod(f1)\n",
      "\n",
      "        outh2 = Unet5(torch.cat((h1,h0,f1_data,sino),dim=1))  \n",
      "\n",
      "        h2 = outh2 + h1\n",
      "\n",
      "        h2_img = fwd_op_adj_mod(h2)/normalization\n",
      "\n",
      "        outf2 = Unet6(torch.cat((f1,f0,h2_img),dim=1))\n",
      "\n",
      "        f2 = outf2 + f1\n",
      "        \n",
      "        f2[f2<0]=0\n",
      "\n",
      "        if see:\n",
      "            num = 0\n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(sino[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('g')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "\n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(h0[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('h0')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "\n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(h0_img[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('h0_img')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "\n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(f0[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('f0')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "            \n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(f0_data[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('f0_data')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "\n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(outh1[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('outh1')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "\n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(h1[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('h1')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "\n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(h1_img[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('h1_img')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "\n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(outf1[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('outf1')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "\n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(f1[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('f1')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "\n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(f1_data[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('f1_data')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "\n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(outh2[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('outh2')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "\n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(h2[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('h2')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "\n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(h2_img[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('h2_img')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "\n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(outf2[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('outf2')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "            \n",
      "            plt.figure(figsize=(10,10))\n",
      "            plt.imshow(f2[num,0,:,:].cpu().detach(),cmap='gray')\n",
      "            plt.title('f2')\n",
      "            plt.colorbar(fraction=0.046, pad=0.04)\n",
      "\n",
      "        return h2 ,f2\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# LOAD MODEL TRAINED WITH BEST PERFORMING TRAINING STRATEGY\n",
      "\n",
      "loss_test = nn.SmoothL1Loss() \n",
      "\n",
      "try:\n",
      "    checkpoint = torch.load('3LPDHybrid.tar')\n",
      "    Unet1.load_state_dict(checkpoint['model_state_dict1'])\n",
      "    Unet2.load_state_dict(checkpoint['model_state_dict2'])\n",
      "    Unet3.load_state_dict(checkpoint['model_state_dict3'])\n",
      "    Unet4.load_state_dict(checkpoint['model_state_dict4'])\n",
      "    Unet5.load_state_dict(checkpoint['model_state_dict5'])\n",
      "    Unet6.load_state_dict(checkpoint['model_state_dict6'])\n",
      "    Unet1.to(\"cuda\")\n",
      "    Unet2.to(\"cuda\")\n",
      "    Unet3.to(\"cuda\")\n",
      "    Unet4.to(\"cuda\")\n",
      "    Unet5.to(\"cuda\")\n",
      "    Unet6.to(\"cuda\")\n",
      "\n",
      "    print(\"\\n--------model restored--------\\n\")\n",
      "except:\n",
      "    print(\"\\n--------model NOT restored--------\\n\")\n",
      "    pass\n",
      "\n",
      "GPUtil.showUtilization()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "--------model restored--------\n",
        "\n",
        "| ID | GPU | MEM |\n",
        "------------------\n",
        "|  0 |  9% |  4% |\n",
        "|  1 |  0% |  0% |\n",
        "|  2 |  0% |  0% |\n",
        "|  3 |  0% |  0% |\n",
        "|  4 |  0% |  0% |\n",
        "|  5 |  0% |  0% |\n",
        "|  6 |  0% |  0% |\n",
        "|  7 |  0% |  0% |\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# SET NETWORKS TO EVALUATION\n",
      "\n",
      "Unet1.eval()\n",
      "Unet2.eval()\n",
      "Unet3.eval() \n",
      "Unet4.eval()\n",
      "Unet5.eval() \n",
      "Unet6.eval()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "UnetP(\n",
        "  (layerb): Sequential(\n",
        "    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
        "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (2): ReLU(inplace)\n",
        "    (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
        "    (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (5): ReLU(inplace)\n",
        "  )\n",
        "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
        "  (layer2): Sequential(\n",
        "    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
        "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (2): ReLU(inplace)\n",
        "    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
        "    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (5): ReLU(inplace)\n",
        "  )\n",
        "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
        "  (layer3): Sequential(\n",
        "    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
        "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (2): ReLU(inplace)\n",
        "    (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
        "    (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (5): ReLU(inplace)\n",
        "  )\n",
        "  (pool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
        "  (layer4): Sequential(\n",
        "    (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
        "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (2): ReLU(inplace)\n",
        "    (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
        "    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (5): ReLU(inplace)\n",
        "  )\n",
        "  (layerUP31): Sequential(\n",
        "    (0): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n",
        "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (2): ReLU(inplace)\n",
        "  )\n",
        "  (layerUP32): Sequential(\n",
        "    (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
        "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (2): ReLU(inplace)\n",
        "    (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
        "    (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (5): ReLU(inplace)\n",
        "  )\n",
        "  (layerUP41): Sequential(\n",
        "    (0): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2))\n",
        "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (2): ReLU(inplace)\n",
        "  )\n",
        "  (layerUP42): Sequential(\n",
        "    (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
        "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (2): ReLU(inplace)\n",
        "    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
        "    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (5): ReLU(inplace)\n",
        "  )\n",
        "  (layerUP51): Sequential(\n",
        "    (0): ConvTranspose2d(64, 32, kernel_size=(3, 3), stride=(2, 2))\n",
        "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (2): ReLU(inplace)\n",
        "  )\n",
        "  (layerUP52): Sequential(\n",
        "    (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
        "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (2): ReLU(inplace)\n",
        "    (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
        "    (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (5): ReLU(inplace)\n",
        "  )\n",
        "  (layerUP61): Sequential(\n",
        "    (0): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1))\n",
        "  )\n",
        ")"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# FIND BEST ACQUISITION WINDOW FOR BEST PERFORMING MODEL\n",
      "\n",
      "files = []\n",
      "losses = []\n",
      "loss = nn.SmoothL1Loss()\n",
      "gt = GTprocessing('GTMouse4.mnc',False)\n",
      "\n",
      "for file in glob.glob(\"M4/*.sino.mnc\"):\n",
      "    if not re.match('Mou.*x1',file):\n",
      "        files.append(file)\n",
      "        data = dataLoad(file)\n",
      "        L = []\n",
      "        Unet1.eval() # Set network to evaluation\n",
      "        Unet2.eval()\n",
      "        Unet3.eval() \n",
      "        Unet4.eval()\n",
      "        Unet5.eval() \n",
      "        Unet6.eval()\n",
      "\n",
      "        batch_size = 1\n",
      "\n",
      "        test_res_denoise_data = np.zeros((35,1,147,147))\n",
      "\n",
      "        for i in range(0,test_res_denoise_data.shape[0]):\n",
      "\n",
      "            h1,test_res_data = LPD(Unet1,Unet2,Unet3,Unet4,Unet5,Unet6,data[i:i+1,:,:,:],normalisation,False)\n",
      "            test_res_denoise_data[i,:,:,:] = test_res_data.cpu().detach()\n",
      "        \n",
      "        losses.append(np.double(loss(torch.tensor(test_res_denoise_data),gt)))\n",
      "        \n",
      "min_idx = losses.index(min(losses))\n",
      "print(files[min_idx])\n",
      "print(losses[min_idx])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M4/sino51min.sino.mnc\n",
        "0.000439726352468\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# LOAD DATA AND GT OF DATA ACQUIRED WITH BEST PERFORMING ACQUISITION WINDOW\n",
      "\n",
      "data=dataLoad(files[min_idx]) # CHANGE HERE ACQUISITION WINDOW \n",
      "gt = GTprocessing('GTMouse4.mnc',False)\n",
      "\n",
      "# miniPET ONLY TRAINED RECONSTRUCTION\n",
      "\n",
      "try:\n",
      "    checkpoint = torch.load('3LPDOnly.tar')\n",
      "    Unet1.load_state_dict(checkpoint['model_state_dict1'])\n",
      "    Unet2.load_state_dict(checkpoint['model_state_dict2'])\n",
      "    Unet3.load_state_dict(checkpoint['model_state_dict3'])\n",
      "    Unet4.load_state_dict(checkpoint['model_state_dict4'])\n",
      "    Unet5.load_state_dict(checkpoint['model_state_dict5'])\n",
      "    Unet6.load_state_dict(checkpoint['model_state_dict6'])\n",
      "    Unet1.to(\"cuda\")\n",
      "    Unet2.to(\"cuda\")\n",
      "    Unet3.to(\"cuda\")\n",
      "    Unet4.to(\"cuda\")\n",
      "    Unet5.to(\"cuda\")\n",
      "    Unet6.to(\"cuda\")\n",
      "\n",
      "    print(\"\\n--------model restored--------\\n\")\n",
      "except:\n",
      "    print(\"\\n--------model NOT restored--------\\n\")\n",
      "    pass\n",
      "\n",
      "GPUtil.showUtilization()\n",
      "\n",
      "Unet1.eval() \n",
      "Unet2.eval()\n",
      "Unet3.eval() \n",
      "Unet4.eval()\n",
      "Unet5.eval() \n",
      "Unet6.eval()\n",
      "\n",
      "test_res_denoise_data = np.zeros((35,1,147,147))\n",
      "\n",
      "for i in range(0,test_res_denoise_data.shape[0]):\n",
      "    \n",
      "    h1,test_res_data = LPD(Unet1,Unet2,Unet3,Unet4,Unet5,Unet6,data[i:i+1,:,:,:],normalisation,False)\n",
      "        \n",
      "    test_res_denoise_data[i,:,:,:] = test_res_data.cpu().detach()\n",
      "    \n",
      "    \n",
      "# HYBRID TRAINED RECONSTRUCTION\n",
      "\n",
      "torch.cuda.empty_cache()\n",
      "try:\n",
      "    checkpoint = torch.load('3LPDHybrid.tar')\n",
      "    Unet1.load_state_dict(checkpoint['model_state_dict1'])\n",
      "    Unet2.load_state_dict(checkpoint['model_state_dict2'])\n",
      "    Unet3.load_state_dict(checkpoint['model_state_dict3'])\n",
      "    Unet4.load_state_dict(checkpoint['model_state_dict4'])\n",
      "    Unet5.load_state_dict(checkpoint['model_state_dict5'])\n",
      "    Unet6.load_state_dict(checkpoint['model_state_dict6'])\n",
      "    Unet1.to(\"cuda\")\n",
      "    Unet2.to(\"cuda\")\n",
      "    Unet3.to(\"cuda\")\n",
      "    Unet4.to(\"cuda\")\n",
      "    Unet5.to(\"cuda\")\n",
      "    Unet6.to(\"cuda\")\n",
      "\n",
      "    print(\"\\n--------model restored--------\\n\")\n",
      "except:\n",
      "    print(\"\\n--------model NOT restored--------\\n\")\n",
      "    pass\n",
      "\n",
      "GPUtil.showUtilization()\n",
      "\n",
      "Unet1.eval() \n",
      "Unet2.eval()\n",
      "Unet3.eval() \n",
      "Unet4.eval()\n",
      "Unet5.eval() \n",
      "Unet6.eval()\n",
      "\n",
      "test_res_denoise_data_hybrid = np.zeros((35,1,147,147))\n",
      "\n",
      "for i in range(0,test_res_denoise_data.shape[0]):\n",
      "    \n",
      "    h1,test_res_data = LPD(Unet1,Unet2,Unet3,Unet4,Unet5,Unet6,data[i:i+1,:,:,:],normalisation,False)\n",
      "        \n",
      "    test_res_denoise_data_hybrid[i,:,:,:] = test_res_data.cpu().detach()\n",
      "    \n",
      "# SYNTHETIC ONLY TRAINED RECONSTRUCTION \n",
      "\n",
      "torch.cuda.empty_cache()    \n",
      "try:\n",
      "    checkpoint = torch.load('LearnedPrimalDual3IterationsEllipses.tar')\n",
      "    Unet1.load_state_dict(checkpoint['model_state_dict1'])\n",
      "    Unet2.load_state_dict(checkpoint['model_state_dict2'])\n",
      "    Unet3.load_state_dict(checkpoint['model_state_dict3'])\n",
      "    Unet4.load_state_dict(checkpoint['model_state_dict4'])\n",
      "    Unet5.load_state_dict(checkpoint['model_state_dict5'])\n",
      "    Unet6.load_state_dict(checkpoint['model_state_dict6'])\n",
      "    Unet1.to(\"cuda\")\n",
      "    Unet2.to(\"cuda\")\n",
      "    Unet3.to(\"cuda\")\n",
      "    Unet4.to(\"cuda\")\n",
      "    Unet5.to(\"cuda\")\n",
      "    Unet6.to(\"cuda\")\n",
      "\n",
      "    print(\"\\n--------model restored--------\\n\")\n",
      "except:\n",
      "    print(\"\\n--------model NOT restored--------\\n\")\n",
      "    pass\n",
      "\n",
      "GPUtil.showUtilization()\n",
      "\n",
      "Unet1.eval()\n",
      "Unet2.eval()\n",
      "Unet3.eval() \n",
      "Unet4.eval()\n",
      "Unet5.eval() \n",
      "Unet6.eval()\n",
      "\n",
      "test_res_denoise_data_ellips = np.zeros((35,1,147,147))\n",
      "\n",
      "for i in range(0,test_res_denoise_data.shape[0]):\n",
      "    \n",
      "    h1,test_res_data = LPD(Unet1,Unet2,Unet3,Unet4,Unet5,Unet6,data[i:i+1,:,:,:],normalisation,False)\n",
      "        \n",
      "    test_res_denoise_data_ellips[i,:,:,:] = test_res_data.cpu().detach()\n",
      "    \n",
      "# MLEM(20) RECONSTRUCTION    \n",
      "    \n",
      "reco_data20 = mlem_op_comp_mod(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "--------model restored--------\n",
        "\n",
        "| ID | GPU | MEM |\n",
        "------------------\n",
        "|  0 |  9% |  8% |\n",
        "|  1 |  0% |  0% |\n",
        "|  2 |  0% |  0% |\n",
        "|  3 |  0% |  0% |\n",
        "|  4 |  0% |  0% |\n",
        "|  5 |  0% |  0% |\n",
        "|  6 |  0% |  0% |\n",
        "|  7 |  0% |  0% |\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "--------model restored--------\n",
        "\n",
        "| ID | GPU | MEM |\n",
        "------------------\n",
        "|  0 | 36% |  6% |\n",
        "|  1 |  0% |  0% |\n",
        "|  2 |  0% |  0% |\n",
        "|  3 |  0% |  0% |\n",
        "|  4 |  0% |  0% |\n",
        "|  5 |  0% |  0% |\n",
        "|  6 |  0% |  0% |\n",
        "|  7 |  0% |  0% |\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "--------model restored--------\n",
        "\n",
        "| ID | GPU | MEM |\n",
        "------------------\n",
        "|  0 |  7% |  6% |\n",
        "|  1 |  0% |  0% |\n",
        "|  2 |  0% |  0% |\n",
        "|  3 |  0% |  0% |\n",
        "|  4 |  0% |  0% |\n",
        "|  5 |  0% |  0% |\n",
        "|  6 |  0% |  0% |\n",
        "|  7 |  0% |  0% |\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# RESULT VISUALIZATION VARIOUS SLICES, DIFFERENT RECONSTRUCTIONS\n",
      "\n",
      "num = 15\n",
      "plt.figure(figsize=(30,30))\n",
      "#plt.rcParams.update({'font.size': 20})\n",
      "\n",
      "# Ground truth\n",
      "plt.subplot(3,5,1)\n",
      "plt.imshow(gt[num,0,:,:],cmap='gray')\n",
      "plt.title('Target Image')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "# Ground truth\n",
      "plt.subplot(3,5,2)\n",
      "plt.imshow(reco_data20[num,0,:,:],cmap='gray')\n",
      "plt.title('MLEM10, direct sino')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "# Ground truth\n",
      "plt.subplot(3,5,3)\n",
      "plt.imshow(test_res_denoise_data[num,0,:,:],cmap='gray')\n",
      "plt.title('LPD3 miniPET only, direct sino')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "# Denoising result with ellipsoids network\n",
      "plt.subplot(3,5,4)\n",
      "plt.imshow(test_res_denoise_data_hybrid[num,0,:,:],cmap='gray')#,clim=[0.12,0.5])\n",
      "plt.title('LPD3 hybrid, direct sino')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "# Denoising result with ellipsoids network\n",
      "plt.subplot(3,5,5)\n",
      "plt.imshow(test_res_denoise_data_ellips[num,0,:,:],cmap='gray')#,clim=[0.12,0.5])\n",
      "plt.title('LPD3 synthetic, direct sino')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "num = 23\n",
      "\n",
      "# Ground truth\n",
      "plt.subplot(3,5,6)\n",
      "plt.imshow(gt[num,0,:,:],cmap='gray')\n",
      "plt.title('Target Image')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "# Ground truth\n",
      "plt.subplot(3,5,7)\n",
      "plt.imshow(reco_data20[num,0,:,:],cmap='gray')\n",
      "plt.title('MLEM10, direct sino')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "# Ground truth\n",
      "plt.subplot(3,5,8)\n",
      "plt.imshow(test_res_denoise_data[num,0,:,:],cmap='gray')\n",
      "plt.title('LPD3 miniPET only, direct sino')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "# Denoising result with ellipsoids network\n",
      "plt.subplot(3,5,9)\n",
      "plt.imshow(test_res_denoise_data_hybrid[num,0,:,:],cmap='gray')#,clim=[0.12,0.5])\n",
      "plt.title('LPD3 hybrid, direct sino')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "# Denoising result with ellipsoids network\n",
      "plt.subplot(3,5,10)\n",
      "plt.imshow(test_res_denoise_data_ellips[num,0,:,:],cmap='gray')#,clim=[0.12,0.5])\n",
      "plt.title('LPD3 synthetic, direct sino')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "num = 28\n",
      "\n",
      "# Ground truth\n",
      "plt.subplot(3,5,11)\n",
      "plt.imshow(gt[num,0,:,:],cmap='gray')\n",
      "plt.title('Target Image')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "# Ground truth\n",
      "plt.subplot(3,5,12)\n",
      "plt.imshow(reco_data20[num,0,:,:],cmap='gray')\n",
      "plt.title('MLEM10, direct sino')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "# Ground truth\n",
      "plt.subplot(3,5,13)\n",
      "plt.imshow(test_res_denoise_data[num,0,:,:],cmap='gray')\n",
      "plt.title('LPD3 miniPET only, direct sino')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "# Denoising result with ellipsoids network\n",
      "plt.subplot(3,5,14)\n",
      "plt.imshow(test_res_denoise_data_hybrid[num,0,:,:],cmap='gray')#,clim=[0.12,0.5])\n",
      "plt.title('LPD3 hybrid, direct sino')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "# Denoising result with ellipsoids network\n",
      "plt.subplot(3,5,15)\n",
      "plt.imshow(test_res_denoise_data_ellips[num,0,:,:],cmap='gray')#,clim=[0.12,0.5])\n",
      "plt.title('LPD3 synthetic, direct sino')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# RESULT VISUALIZATION ONE SLICE DIFFERENT TRAINING PROCEDURES\n",
      "\n",
      "num = 23\n",
      "plt.figure(figsize=(30,30))\n",
      "#plt.rcParams.update({'font.size': 20})\n",
      "\n",
      "# Ground truth\n",
      "plt.subplot(1,3,3)\n",
      "plt.imshow(test_res_denoise_data[num,0,:,:],cmap='gray')\n",
      "plt.title('LPD3 miniPET only')\n",
      "#plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "# Denoising result with ellipsoids network\n",
      "plt.subplot(1,3,2)\n",
      "plt.imshow(test_res_denoise_data_hybrid[num,0,:,:],cmap='gray')#,clim=[0.12,0.5])\n",
      "plt.title('LPD3 hybrid')\n",
      "#plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "# Denoising result with ellipsoids network\n",
      "plt.subplot(1,3,1)\n",
      "plt.imshow(test_res_denoise_data_ellips[num,0,:,:],cmap='gray')#,clim=[0.12,0.5])\n",
      "plt.title('LPD3 synthetic only')\n",
      "#plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# RESULT VISUALIZATION ONE SLICE RECONSTRUCTION ALGORITHMS COMPARISON\n",
      "\n",
      "plt.figure(figsize=(30,30))\n",
      "\n",
      "i=23    \n",
      "plt.rcParams.update({'font.size': 20})\n",
      "# 10 iterations MLEM\n",
      "plt.subplot(131)\n",
      "plt.imshow(gt[i,0,:,:].cpu(),cmap='gray')#, clim=[0,0.6])\n",
      "plt.title('Target Image')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "    \n",
      "plt.subplot(132)\n",
      "plt.imshow(reco_data20[i,0,:,:].cpu(),cmap='gray')#, clim=[0,0.55])\n",
      "plt.title('MLEM(20) Reconstruction')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "    # Denoising result with ellipsoids network\n",
      "plt.subplot(133)\n",
      "plt.imshow(test_res_denoise_data_hybrid[i,0,:,:],cmap='gray')#,clim=[0,0.55])\n",
      "plt.title('LPD Reconstruction')\n",
      "plt.colorbar(fraction=0.046, pad=0.04)\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# OBTAIN TOMOGRAPHIC RECONSTRUCTIONS\n",
      "\n",
      "volume = volumeMerger(test_res_denoise_data_hybrid)\n",
      "gtvolume = volumeMerger(gt)\n",
      "mlemvolume = volumeMerger(reco_data20)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# LPD TOMOGRAPHIC RECONSTRUCTION\n",
      "\n",
      "from matplotlib.gridspec import GridSpec\n",
      "# Setting up the plot surface\n",
      "fig = plt.figure(figsize=(20, 10))\n",
      "gs = GridSpec(nrows=2, ncols=2)\n",
      "# First axes\n",
      "ax0 = fig.add_subplot(gs[0, 0])\n",
      "ax0.imshow(volume[0,0,:,61,:],cmap='gray')\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "# Second axes\n",
      "ax1 = fig.add_subplot(gs[1, 0])\n",
      "ax1.imshow(volume[0,0,:,:,62],cmap='gray')\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "# Third axes\n",
      "ax2 = fig.add_subplot(gs[:, 1])\n",
      "ax2.imshow(volume[0,0,23,:,:],cmap='gray')\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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WMXNxzFptactd6zZV2ldbCdtnmHX1pNNbd4jC4cOH+yx97T0losL6y7qelfzn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WzlW+J+VLWyzN7eXmF/nb9FevXG8hJY6mPtzS7zLMTyVVnPV837Fl+nvJ9T3sqW8mnc/pN6Wpd5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\nZCV0xUgSQkhXaWDCY+S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ivdRtWhtte9NLlRPN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kUIQVU+02/8Lz9Gi/iQo1JOwXlWR8qGzUEXnn3W9Un7V/uOz18rApXGYXSMjJAVQigp9+kz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\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qRK2+t3P3+KCFZKAcHqDK4odsm0YaQ/o9+eSTAIBr164B6PLt22+/DaA3kc1PJkw9evRo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\np9XpKt+56Rnac2Dbt2uaJk5OTpbGUOq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/PyL+KCL+YNsrADA1yrQA4xJnAcY1lzi7ckXW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\nUu7Tdboan4O6HFN2q9gVH8vzYux7jQcPHiyWmbvtzt0Nrmo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8hCp6k6sN+0PRpukvS0HsOgLAQz+yVJB0MIz9nrsgDAJMzsSyV9Twjhmr0uCwBgcmb2LZK+sx1y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3A/fRJuV3S+1r2Ynj5n1yVJeOg0duZIUYFolnXZHlcDgOBItCkg6Hw7GocJ51OByO5uFc63A4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       "text": [
        "<matplotlib.figure.Figure at 0x7fdab082d320>"
       ]
      },
      {
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       "text": [
        "<matplotlib.figure.Figure at 0x7fdab09e5da0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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2/tuR/URERACAibHdo+qvq/dXjMs4RkR+AeBshIsq/orW535Hqepf0WoH+kDh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\n+gWPv/GFnz3NpMSnWP2+jeuuT0o9qhgxuOOOO+b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       "text": [
        "<matplotlib.figure.Figure at 0x7fdaa20e9cc0>"
       ]
      },
      {
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mPeTPyftTfd+7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\n4CEOAAAAQAc8xAEAAADowP8B82ryk1yYYmgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fdaa20e9c18>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# TARGET IMAGE TOMOGRAPHIC RECONSTRUCTION\n",
      "\n",
      "# Setting up the plot surface\n",
      "fig = plt.figure(figsize=(20, 10))\n",
      "gs = GridSpec(nrows=2, ncols=2)\n",
      "# First axes\n",
      "ax0 = fig.add_subplot(gs[0, 0])\n",
      "ax0.imshow(gtvolume[0,0,:,61,:],cmap='gray')\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "# Second axes\n",
      "ax1 = fig.add_subplot(gs[1, 0])\n",
      "ax1.imshow(gtvolume[0,0,:,:,62],cmap='gray')\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "# Third axes\n",
      "ax2 = fig.add_subplot(gs[:, 1])\n",
      "ax2.imshow(gtvolume[0,0,23,:,:],cmap='gray')\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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cH+FUAAAAADvgIQ4AAADADsiJAwBw4l6HP64t5b2XXDPHDlk/NlXf3i5D5GsI\nc4aNd+HNGdZdq3B2lUmn8uoMwzhHTg3PzxDsGgY/VdG0vhfyeDJsfxjGx1PHOFV9tPZZz1fqcgHN\nDbteUol2bZoFYJ7FD3F82MbmfqEdI0nV1uXMt9rfki/5LhfJ1I9W96NY+8h8MDVJXSbP+/jjj0dt\n+WNdk8hNlSqv/eePfOaaGYbxDcfNmzdHbXXd1P3IT52Her5yXLWPvBGp48q2zz//fNT20UcfHZZv\n3bo1asv9Z/6amqPmH/7hHw7Lv/nNb0Zt//RP/3RYfvjw4agtr0ftc+4Nzdwb/iU5nab2VXVj7Pbd\n9X/WeXzO8z9MAABwmQinAgAAANgB4VQAADvRheucRTW6s5yRvSQ06ZRCqKbaTmmm4tzy0GvHnDNj\na7hTzjiuoUm5vzpbOF9PVQodhvHM6Cw3PgzDcOXKlcn+U1cJte5vqq32kWq4Vp6TrpR3N7O7ViPN\nfnJcObN6GIbh2bNnk2POPrr3whbvIdEeb3aq3yGcr6M8xNlqav8eTX241oZabGXNNVgbArbkS777\ncZi6Ma0hM/m6tnVlvnPfdbtUf+Q//fTTN25Xw6nydT3/WWK8jivDhR4/fjw55hrmNXWOuhukGjKV\nYVF3794dtdXXU+pNRMamZ1uGsw3DONTqt7/97ajt66+/ntyunocp9TxPhZyt/b7qvveO8R14jO+N\n7j+Dc4/hMn3f752bQQCA/RFOBQAAALADHuIAAAAA7ICcOAAAJ+4sw99OJSxySbVA4YF/aetz0oW9\n19DmLtw8Q8NrRcsakp2mwsZruHSOpb5Psq3uO8eVoeDDMA6Drzl+pvqv48o+a96bXLeG+HdVS3Pd\nLqw72+o5vnHjxmG5lh/PnEJdnqCqy4mzpgrvqXwnnQfngTdZ/RDnor+J5n5g1v5ALskbM9dZXpOz\nzuPT5T3JH8WaKC5/aOuPaf4o3rlzZ9SWeXAyCd4wjG9GPvzww8lx5ZjrD34mkas/5E+ePHnjesMw\nzmEzNxfM9evXJ1/Xts8+++ywXMuIZ06cekOR74d6rJnDJnPb/O///b9H6/3n//yfD8s1J879+/cP\nyzVJYe6v3ih2N3JT4197s3HshKJnkWtszY3VMfpnO2sSUc79bgEA4OwJpwIAAADYAeFUAAAn7vVM\nqcs0o+0YM6HX9rnFeT92qeAu3GltP3NLRddZxd04crZznSWdarhOrpuzpOsM4DzPNXQo913bupLg\nOeu7Vi3N/eex1tnBOf6ujHgdR86arucyz1ENhcox51jqOe/Kuuc56sqP1+Pp3utrwoMuc+Xjy3Ss\nzLf6Ic7a8tN7dIwf2u7Lbe2s7u0fAAAgAElEQVQNxtybj7n9LwkPmfsjv6Rs8VRbt01XYvzFixeT\n/dTwoAwdyjjoYRj/wOUPZN13/vB98cUXk/v+1//6X4/a/v7v//6w/O23347aMjSp/lhnCMS1a9cO\nyzXmO3+8c71hGB9PDbXKfuo1zhCnGgL2u9/97rD8X/7Lfzks//rXv55cL8uND8P42nUhU10J+7nv\nvbWltY9hi5ugrcYv38TF0P0WuMYAAKdPOBUAAADADniIAwAAALADcuIAAJy4NSHG79r3RbD22PZy\nTs4zDLILpe9yz6QlZbgzvD3D4GtFvS6UOsdZw9K7nDgZyt2VAM8w/nrcuV4N4+/C4O/du3dYrjlr\n8nw9fPhwsq0rYZ7nrzuX9Xrk+epyG80tfb7kM9dtN/czcUqf8VMaC/uw+CHO1Adjj2++7gvg2Mnn\nOlsk69squd3cPo59k9Tlceh+mDI/S80v80//9E+H5U8//XTUlvlg6o9w/kBnYr2aQyZLdNc+csz5\nI1v7rD/kt2/fPixn3p5hmD6XNeFf3ojUPrK8ec1Lk+er5hfKXD1fffXVqO0f//EfD8tZVvybb74Z\nrZc3H/XGKq9rvcHI1/Umomub+x+i7nsi+1ySy2rN52XtZ+yUkgGuvVljvWMkhgUA4PwIpwIAAADY\nAeFUAAA7sdUstr3PhjvG+PdwTtZWKe3W3aIUeQ21mVsdsu67C73qypinDDmqFS1zNnWV++6O58aN\nG5NtGfJVZwB3pdUzvKpWRc1Z2TUMK4+nnuep0ud11nqeo7rvnNFew83ydZ0l3enC1lIXopX2Mqt0\nD98v7MdRHuLs5U06N2TqGNPR18ZrbrHdFqWJl4RrdW35Bb322LrreP/+/cl+uh+fr7/++rD87//9\nvx+1ZUhTypCiYRjfDNSbkvwB/fDDD0dt+SNf95U/1lnWexjGP9YZJlVDsjKsrIZM5Q95DcP6r//1\nvx6Wf/Ob34zaMoTq+++/H7Xl68ePHx+W641U7rtej+4a53uo/uDPDXfqbhS6UL653yFzb0Q6S8Ki\nTqUsetX9p2EvvxsXiXMOALA/wqkAAAAAdsBDHAAAAIAdkBMHAAA2cIxQ0a3y4KyxNqVAhhHXEOkM\nKa995Lo1/0vmnskqlrXKZ465luTO0O1aCTP7r/vOPDhzUx3UfDx53F1p9S40vOazyYqmNQQ/181j\nqyH+mV6g9p+pAur52iKvUmfvFS33OGb2Y/VDnCW5IfbwJp6bQ6auO/eLfG0+i5pYbW5Z9CV9TlmS\nJ6hbt0t21iXFy31kH90Pfk2Wlj84NT9O5mR59OjRqC3zzdy8eXPUdu/evcPynTt3Dss1t032X38U\ns/x4/VHsyqLnTUXdXx575p6puXqyVPh33303avvv//2/H5b/+Mc/jtoePHhwWM5y4HXM9XjydV6r\nek7mlpHvPktdLp0tcsjMzZ1TLfn8T33Gt8ir0/W/xNpcXV0fnL2p71zXBgDgdAmnAgAAANgB4VQA\nACfODKnLa4vZ7seuprqkj24Wds44rseWIUc5m7dW01xb3jzDnzJMqerCnXJGcA3zytCu2n+GONUQ\nsOynVhzNfups9DwvL168mNx3blevR7bV/nPdbjb9MaIWTtUex8w+LX6I8/rNubYkd+c8P7xLfqS2\n+EGb+wNTfyjmxkV3ZZjzB6Z+6c4d11pLQlqmQmi6cLD8karb1ZLcGXJUw5Z+/etfH5b/43/8j6O2\nDK/6u7/7u8Pyv/yX/3K0Xr6+fv36qC2PtY4rw6Q++uijUVuGaP23//bfRm157E+ePDks17Co//k/\n/+dh+R//8R9HbRlW1sWw11CoLl576jrW9/bacKe1n5e5oVZdf3PDG49RDnxtWNTcdZeEmE6tt9ZF\nuJHbg61C9AAAODvCqQAAAAB2QDgVAMCJez1DbauE5Gzn2BWijm1tuEtaO7OvVnDKUKLa51S1pRoe\nlOvVWcU19CrV8KeptlrdaWoWe52ZnDO5a9GMPO56njOMqYZT5XZ1f9mWYWp11nq+rtcjz18XRrbW\n3FnFZujCmJk4AAAAADuwOidOZ4tyumutfVK7pGRuN+aptiU5MWrisNTls5lbjnjuX1KWPBGfu7+a\nrK3b31Tyue4vAbWPur+Uf1Wpf6nJEto1X86NGzcOy5lf5vbt26P1utw92UeXiK7K46t/Lcntsq3+\nxShLftd8PNnH2r+4zC03vyTZ4Nz3YnfOu1L33b7Tkpw7uW73We1s8VfPLo9PtcW4tsj/4y9s72br\n31UAAE6HmTgAAAAAOyAnDgDABWDm1XZONQfHkpnjc/vZoo8qZ6B2s6Lr7OPss27XzbZNuV2X46WO\nP/PG1Pwyue86gzr3l33WyqS5Xp0dnK9zxnT1ww8/jF5nbp16PDlD/OnTp4flOpM3r0E97rk5cZZU\nHJ1rTeTDWTuV7wUun6UPce4Pw/C7YdjuTbv1m/8sxnXscIH6BTqlCw9Zu+9jWzvmtbrQpE5XMnvq\nx/Wbb75ZtS+2s8X76xjlupck/zv2d+IWSVHdtJy2Da7Pr7YYBwAA21v0EOfnn3/+5FgDAQAAAGCa\ncCoAgAvgVEOA9uLYIRrnWYp8qzCsqT5r/134Ub7uwnDWzq7tZr9msYclxTDyeLr+uz5SLWeer+u4\nMiSs7juPp57nHHOGU1W5Xd13FzI1td4w9GFYU58t318wn8TGAAAAADvgIQ4AAADADniIAwAAALAD\ncuIAAJw4+SGOb+457vLJHCP3zFmbynXTnZ96nFnZs+a2ycqhV69eHbV1eXAyt0rN/zK171qWPF93\nuWe661bbsmpp5sSp6+Wx1jwxeTx1zHlOvv/++1FbnsvuGuRyrbKa/S8pyZ7H0FXC3KpK5qnY+/i5\nGMzEAQAAANgBD3EAAAAAdkA4FQAAbGDv4VNr1+vaavhOhhx15cdraNVUKFQNTcrQoVryO9vqdi9e\nvHjjerWfuSFzdd8ffPDBYbmGTE2tV/up4+rCw7JseW5Xx5shWbk8DONrUPe9pox4tSREb03/HSXN\n2TMzcQAAAAB2wEMcAAAAgB3wEAcAAABgB+TEAQC4AOR0eLvMg+F8vdlULpKaxyXXq201R8qULjfM\n3LZ6HbOt5njJ1zVXT+aQqfl4umOdeh/V85j5frpy4NWVK1feuFxfv//++5N9dv3ntar5eNIxyoj7\nPMI6ZuIAAAAA7ICHOAAAAAA7IJwKAIBLIUM2uhLDeykVvrZE81xdH9nWhU/VUttXr149LNcQoAxV\nqn1OhRW9evVqct9d+fEMnxqGcahVDadK9XhyH9123fnK17Xseo6zhlPdvHlz1v5yzDUcLMdcj60L\nAevCqabWe9u6U5a8l4VlcRmYiQMAAACwAx7iAAAAAOyAhzgAAAAAOyAnDgDATsn/sF49d3vJgzNl\nbv6arcx979Wy1ZkHp+Z/yXwtNXfLjRs3DsuZQ6bm1cncNktKeWfumVqavCt3PpVLqZ6fzD1T+8vX\ndYx5/mrOmswHVPd3/fr1N25Xz3luV9tyu7rvHHM9z3NLzM/Nq1N151nZci4DM3EAAAAAdsBDHAAA\nAIAdEE4FALATwgO2s/fwqWE4ThnxfD21XF/X8KAaejO1XRdOVcOkUlcqPHUhTT/99NNknzWkKV+v\nDVvrwpbyuGvb3NCkKo+vG1eek1rCPF/X65Gva2n1PNa5418bklUd+zty7jXuSrLDuzITBwAAAGAH\nPMQBAAAA2AEPcQAAAAB2QE4cAICdkGdhmYt2vo6Rx6fLddP9e5fHJdV8Kalud/Xq1cNyzavz8uXL\nw/LccuA1t0221fdCttV8LHPzs3Ql0vNYl+R7yfNez2Weo5qzJtu6c5LrXbt2bdR28+bNw/KLFy9G\nbXnea5/d8c39DJ51qfAt9ncR8myxD2biAAAAAOyAhzgAAAAAOyCcCgDgxO09DOgszQ1pqOf0ooVC\nrC2FPXebfF3DZzLUprblef/ggw9GbRn2U0uH5+vsswvXqnLdul2+rmFeOea6Xa6byzWcqvsM5/HU\n85V91mvQhVNNjauu170Xct0a3taF02Vbd326ktydY3wfnmVpct/nvCszcQAAAAB2wEMcAAAAgB0Q\nTgUAwIXUhS2cRaWnKXsJp9g6JGsYxqE3r169GrVl+M7z588nt8t937hxY7Rehu/U/vO814pKXRWo\nuSFBaUmFq66qVRfSlPv+6aefRm15vrpwsLlVuer56uS5XPuZyNd7+bzAWTETBwAAAGAHPMQBAAAA\n2AEPcQAAAAB2QE4cAAAujLMsFXwW5u5vq3GtOX/dNjWPS+ZkyVLkwzAuI16P58qVK4flLodM5sGp\nbTmWq1evjtqePXv25gN4i6mcMjW/TB5PVw68tuU4a1uX6yZf53Y1d04nt6vl4HPfXb6frjT9Fnlv\ntvq8KwHOnpiJAwAAALADHuIAAAAA7IBwKgAAOEM1xGSL8I3ax5oyz926tf8M16ltGWJUS1O/ePHi\nsFxDmrpy2hmyk221jHiGZNUQowxbyvCsYRiHGdXwoK4M95R6HnPfNcwr1+1KjNfzlX3W83Xt2rU3\n9ln7z3PUjbmGctV+0rHLg59lyOQx9nWMzz+Xi5k4AAAAADvgIQ4AAADADniIAwAAALADcuIAAMA5\nOnbZ8i5fztqxdHk8Mm9MV/p6bl6VYRjnvsnl2n/mtsmcLrXPrvx411Zz4tScP691OWRq/12+nG67\nzJFTS4B3Jc1T5hCq56u7Ptm25P07N1/OeeaJmbvvrXLbKG9+ua25/mbiAAAAAOyAhzgAAAAAOyCc\nCgCAS+HYYUvV1NT4sxhH7nvt/roy5V2fue8a7pQy9GkYxuE8f/rTnya3yz5reFMNCZoaV702dSxp\nTYnxLiyqynNZx9/trwvDyHOUx7akVPjacurHLjF+ls76OwPmMBMHAAAAYAc8xAEAAADYAQ9xAAAA\nAHZAThwAAJhpSbnuNX1ulYNjbj9z1+vyo9S2zMdSc+Lksdb8LF0ulczdktvVfefr2ke21Rw43fGt\nyfFSjzv7n5sfZxj68uBZ3jzLoA/DdG6g+u+5vyzdPgzjY62l1LNtbu6cut0xt3kXc/M9yZfDeTET\nBwAAAGAHPMQBAAAA2AHhVAAAsNLW4SFd2xalwtf205WR7vqroTbd8dWQnZQhSLncldau/eXrOuYM\nM6rhTlnuvIYtdSXUU46z9n/lypXD8tWrVyfH1YVM1T6zLUOyajhVbnfnzp1R28OHDw/L9TjzXC65\nxlNl0Ze8788yvGrtZxXmWvM+MhMHAAAAYAc8xAEAAADYAQ9xAAAAAHZAThwAADhRW5cw36qPrs/M\ns9KVH89cM8MwncflTf28VvPeZJ/dvrtS3nVcmYuma+vkekty26Sal+batWuT203lDaql1fM8PH78\neNSW57Yed46le28c470Hl52ZOAAAAAA74CEOAAAAwA4IpwIA4FI4RvnuY1s75q4E+Ny2LiymG9dU\n6FPdrq6XpbarqTCjGpqUYVFdyfIaHtSNOUOQunLaeS67sKgqx1nDnWpJ8Kntrl+/PmrL85ChT3X8\nr169euPyMAzD06dP39jHMKwvDz51ns+zjHjnVMfF5WYmDgAAAMAOeIgDAAAAsAMe4gAAAADsgJw4\nAACwQ11um6n13rZuyvwla3MGdfuuOXCyreZgmSrRvSQfS3e+MkdOzaWT69YcNVOltuv481x2+XJq\n/x988MFk248//nhYfvny5az91Zw7eay1j1x3SU6kTp7LtXl19p6XZu/j5/yZiQMAAACwAx7iAAAA\nAOyAcCoAAC69uaFJe1SPZ4vjyz5q2egMw+lKU3flumtIU7ZlWFHtP7er4UcZVlS3m9rXMIzDt7pz\nl+FNVfZRw6myHHgtFV5fpzx/tTx4nocMW6sl2buQrDxHXXhYdx23CB06i/CjY3/+hVCxJTNxAAAA\nAHbAQxwAAACAHRBOBQAAl0gXOjLV1q3XhTTVUJt8XcOdskJU3S7l/rr+u8pYdd9T69X91VCoqbba\nR4Y0Xb16ddRWQ5xS9lnX66p5ZXhV9lHPSZ7zGpKVoVZrw+KqNWFFx6hONbcPYVCcIjNxAAAAAHbA\nQxwAAACAHfAQBwAAAGAH5MQBAIDQ5VI5JXPH1R3P3GOt63U5UeaOq5bkztwtNWdNl8MmdaWvc1w1\nv0y+7sqbZw6Z2jY3f03Nq5Nq//k6S5HXfdTzk8eex1OPLa9BvR5zS8V3OWtOqcS4PDhcFGbiAAAA\nAOyAhzgAAAAAOyCcCgAAGl3oyB6sHXMXVtKV8u72nSE6z58/H7XduHHjsPzBBx/M2l/tP7eroUMZ\nEtSNuYZCZT/ddl04Vb6uYUsZClVDpnJ/L168GLVdv359crvsM8953XeGa9Xzla+7cKpTDT86z3Gd\n6jnhYjATBwAAAGAHPMQBAAAA2AEPcQAAAAB2QE4cAACYaUn+l1OV4+zG3JUiX7OvYZifX6Yr8526\nXDB1m8yrU3O85HZdOfNatjxfZ96bWkY8j6fmy7ly5cph+fPPPx+1vXr16rBc89nk67nnYW6J9Koe\n99zPQbdd14ecMjDNTBwAAACAHfAQBwAAAGAHhFMBAMAGagjIXsKr5lhyLHkeunCaGr6Tr7tQqGyr\noU9dW4Y01fCjGl6VurLlOa4uHCnbrl27Nmr74osvDss3b94cteW6X3/99agtx1L3PRXaVder1yfN\nDbtba4twvUoYFpeBmTgAAAAAO+AhDgAAAMAOeIgDAAAAsANy4gAAwA4cIy/JMfL4dCWmU23rynB3\n+WZSblf7yHHVHDj5ust705UfT5lHZxjG46/5eF68eHFYvnPnzqjt5cuXh+UsRV77qddtKi9RPbZj\nOHZemj3kvemuB7wrM3EAAAAAdsBDHAAAAIAdEE4FAAC0anhIV366KzHehTT99NNPh+UaPpWhQxnS\nVNfrQrIyHKk7ngxhetO6U+PK/dUy4rnvetx/+tOfDsvffvvt5L5qKFeWI+9Kh+d29Zx0pdXzOp5F\nKB8wj5k4AAAAADvgIQ4AAADADniIAwAAALADcuIAAMAOLCkHPjeHydz1uvwlNe9N5lmpbZmfpZbM\nzrFknphhGOdyyfLddb3MiVP3fevWrcPy1atXhym1PHge+6tXr0Zt+TrHeP369ck+a16aHHMtAZ7n\nqPaZ/dR8OXleso/af673+PHjUVtej5pzJ9vqe2NNrpsl23T7PlV7HDOny0wcAAAAgB3wEAcAAABg\nB4RTAQDAERy7RPOx5fiXhMzMPdYupKkLk8p9d6W1uxCg2pahSTXcqYZXpefPnx+Wf/zxx8n1cn91\n3/m6C8Oq4Wc5zno9uj7T06dP39jfMPSl3NNZhwcJR+KyMxMHAAAAYAc8xAEAAADYAQ9xAAAAAHZA\nThwAADiyi5Qfp6rH0x1flh+v62UOli7HS6p5XDL/Sy27nfll6r5/+umnyTHnPuo48nhSV8q7limv\nuW5Snoeac6fL8XP79u039vf++++PXj98+PCwXM9X9l/Pl7w0vXp+nEu2ZCYOAAAAwA54iAMAAACw\nA8KpAABgB7owpbVta3Xlx7PMdy5XtS3DimqYUob65PHUEKCp9YZhHE5Vw526kKYMVarHmmXSu/Oc\npcjrcWeYVO2/K32e5+ju3buT+84+cxy1rQvzqWOeGwK0pDT9qdgi9HGPIZPsh5k4AAAAADvgIQ4A\nAADADginAgCAIzvrsIy523VVdLr18nUNfcrXXZWmGqKTbXUc2WeuV6st5Xq1LbfL0Kq6bq30lOFO\ndVx5fNlnPbYMu+rOZXfcdVwZXlWPNff/ww8/HJYfP348Wu/FixeH5Tw/9XWtjJX97zFkqpPXYO7n\nA86SmTgAAAAAO+AhDgAAAMAOeIgDAAAAsANy4gAAnLjXeRj2nmuC09OVle7yf8zNidKVGP/pp59G\nrzPHS90uS4fnejWPS+aQ6Upy12PLEuNdPpunT5+O2qbKg9f8NXmstS1z6XQ5WOrx5OvMbTMMw/Dy\n5cvD8v379w/LmR9nGMYlx1+9ejU55iUlxrvy83P7WLPeEuf5Xep7nHdlJg4AAADADniIAwAAALAD\nwqkAAGCH5pY7XlsWeW24SxdC1YVhpVoyO0OaannwlKFKtf9sq2PMsKhr166N2mrIVpoKOarnPNtq\nOFWWKa9ttXx7evLkyeR62fbgwYPDchd2lcvDMD622v/ckKm5ziLE6FTCmJZ8Hk9lzJwWM3EAAAAA\ndsBDHAAAAIAd8BAHAAAAYAfkxAEA2ImuHDQX09yS32c5jvq65pfJ/Cm1jHiWsa5tXTntbMu8MTU/\nTuavyTw69XXmwBmG8fHUcWV58yrHmcddz0nNdZO63DP5uis/XsuDP3v27LCcZcRzue679pG5errr\nv8TWuXTm7usYfZ7n55HLzUwcAAAAgB3wEAcAAABgB4RTAQDsVE7nF1p12i5aGMbccKoaHpQhOrWk\n9a1btyb3l/1kH3XfGd5UQ5jyvNcS5jmWrmR6DbWqYV9TMiQrxz8MffjZ1HHXdWsoVB5Pho7VPjL8\nrJ7LU/1OOdXP0qmOi4vHTBwAAACAHfAQBwAAAGAHPMQBAAAA2AE5cQAATtzrXAtdngXlx8/HHvIS\ndeOq75up91i3Xve+rHlWMidLl8el5qyp5cJfq3lvuvwyU2XK6/5q3pgcZz2XU/l/urLk3We1KzFe\ncwjl6y7PTl6Dej3ydVfCvJr7Xj/rMuJnmYumO7a14zrV7xBOi5k4AAAAADvgIQ4AAADADginAgA4\nca+n4ptqf3rmXpNjhHl0JY27ti3Gkv3Xc9C1ZfhOV+66hkJ98MEHh+Us613Dg1LtP9XwrDwnNaRp\nzbFmWe+36fb94sWLw/KzZ89GbXm+6nbZZxcytUVYVNfnWYd5nsp35JLP2KmMmf0wEwcAAABgBzzE\nAQAAANgBD3EAAAAAdkBOHACAnVBGfL+6PCFr+tiqnyW5dOaOq8shk6W9M7fNku3SzZs3R68zD07N\nqzNVDnwY+pLgmd+mlj7PY6h9TqnHlrltMgdOfV2Pp8v5M7fE+Nz8RVvl0lnznXXRvucu2vFw9szE\nAQAAANgBD3EAAAAAdkA4FQDATmW4iyn6+3GMsKiqe2/MbTu2JeXBX716dVjOMdYwqBs3bszadw1N\nyrF05brr+ckwr1q2PGVIVh5L3V897nw9N1yr6s7zXEtC5nBOOC4zcQAAAAB2wEMcAAAAgB3wEAcA\nAABgB+TEAQCAc7RF+fFj9LlFGemajyXzutRxZX6bWn481+3KiGe57loOfG7J7FrOPMcyNzdMPe7s\ns+bxyXVrTpw8ntpnl+tmqiR718eSti4njnw5cFxm4gAAAADsgIc4AAAAADsgnAoA4MS9DknowmJq\n29owBmXLL565pcjrunPDsLr16r4znKqGQuW6165dG7VduXLlsJzhTR988MFovS4EKNVwo3xdS3nn\n67mhY0tKt+e+axhZvq7nK7ebW5q8rpdtXThVd9zH0L0PfS9x2ZmJAwAAALADHuIAAAAA7IBwKgCA\nE/c6nGBJWMxawhjO15IwnGPvb+77bW2oTQ3fyUpMr169GrXduHHjsJyhVd2+67nrwrBSd867cKSs\nOlXHlcda+8/XeWzDMA6h6s5XVwWsjjnlOGsY2dxwqrP+Xujee8JBuQzMxAEAAADYAQ9xAAAAAHbA\nQxwAAACAHZATBwBgJ5aUct6CfBPv5izP15JcOnPzHs3tY8mxZZ+1zHeqOXFevnx5WM7cLZkrZxjG\neVxqmfL3339/si3Hcvfu3VHbixcvJseVeWnyPNRy4Dmup0+fjtq6vDRrZX6etblt1l7jNe/1tbmg\njp0zCk6RmTgAAAAAO+AhDgAAAMAOCKcCALgAziLUSgjVemtKd7+LNWFRa/e/NgynhlNl2E8NK/rx\nxx8PyxkWVfu/devWG9cbhnFZ8VxvGMbhVbX8+PPnzw/LNRQqQ62ePXt2WL569erkehnqNAx9iFm2\nZYn0YRifr7pdhnmlY4RMbfG9cIywqLMOP4WzYiYOAAAAwA54iAMAAACwAx7iAAAAAOyAnDgAABfQ\n3JwPXYnpy+rYuTS6c75Fjpp3MXV83Ti646m5bbL0di3XXfPIzFHzy2RumHosWY789u3bo7YsK565\nZmqftW0q103m8Klq/prMdVOPJ9v+/Oc/j9py3dqW48xzXq/H2vLjwPkxEwcAAABgBzzEAQAAANgB\n4VQAACfumKEMwiT+UhfStCT8bG6Z77MOmTpL9fxk2E8tg51tNWwpZRhRhgoNwzi86fr165NjydCq\nYRiGK1euHJZrafLsp+4vS5NnifGqKweeJc1rSNlUqfBh6N83GTbVhUXNbQNOh5k4AAAAADvgIQ4A\nAADADniIAwAAALADcuIAAOxU5sSQv+K0zc2Pc+x9r93/MUqf17w3XU6czCMzt3x3LaeduW6ydPcw\njPPS1Hw5qebEyTLpL1++PCw/efJkso+q5shJc3MuVVN5nNbmvblo+XKW5Laa2m7v54D9MhMHAAAA\nYAc8xAEAAADYAeFUAAA71YXomOrf68KB1oawrN3fsW2x7y6cpoY+zT23dbt8naFVwzAutZ1hUXW9\nDLWq5boz9ClLkQ/DMNy5c2eyzxxXljev22WfXenzLnyqhoDl63q+5oZT5XZL3tu+Q873cwtTzMQB\nAAAA2AEPcQAAAAB2wEMcAAAAgB2QEwcA4MS9zstwjFwtl1VXMnur/EJb59NYksdn6zw4S85Jts3N\n21Jf19wwWb4789Lcvn17tF6WCq+lyB89enRYrmXEc7s6rhxL5tWp48q8Pbk8DOOS5rWPzJ9T897M\n1ZVrz+Uu584SF7nU9tp8WXBWzMQBAAAA2AEPcQAAAAB2QDgVAMBOLAlpucjhDsfQhQ7NDa84djni\nJSFg56kLp+pKjHcy7Kcru51qW2737NmzUVuGO12/fn3UluFPNUTryZMnh+UsI15DpnK7rlR4LWHe\nmTondX9duNbc0LeL5in6ekkAACAASURBVBjhh3BWFj3Eee+991Z9st1EMMdW8edbm/ulfirj3Yp4\n4Lf7q7/6/yYzdjepsDc///yzu1kAgBO0eiZO9x+8/I/NMIyfKNckX1vsf+5/Nrsnrv7D9W7yrxdV\nPvGvT//zGmRCuWH4y790nJfu2LoEfHP/ClXl56Wul+fvGA+Xss8rV65M7nurz/Ep6v76Wr/b8j2b\nf+V602vG569LGgkAALzZ4oc4U/9x7P4ze4yb8y2m/p3Kg5u1M1DqfyjXnJO1sy3qdvkf/CUPLLKt\nezBwjFkhcx/irf3PeF6feq3mTv1dW4VkboWN7rzWacCdbkbKXOf5eezeC92076z2sMW+q7Wf41P5\nbqvWPITk/9rqGudD4m5KPQAAp0lOHACAEzdVYtwD0ePY6jyfZb6cY1vyoLf7w2I3Qzv/cFb/AJYz\nhLuZsPlHqPfff3/UlrO8M39N3V+WDa/91JniT58+feNy/eNkvl4SHZB/DK9/PMpjr+Oayhu0toT5\nHi35HPtDBnuiOhUAAADADniIAwAAALADi8OpXk81W1vick7fl82ScpFzyzJuletmi/63uP5ry352\n/W+RS6frb27+j7XXv7NFLpUl077X5u6ZGsvaZMynmo/nPPNXnao9jvk8bXW+uhx1U+E6nJatft9d\n52W2+P09RmjVFr8hx9bdf9XvpAyFqm1ZaCLDm9Z+JmqJ8bxvqyXGs4x4zRk4FdLUFeiooVZdqfA8\nhhpOlfvo+pxbbOMifC+s/QxehGPn8jATBwAAAGAHPMQBAAAA2IHF4VSvpzIuKRt+jOlpW4RQTPW3\nVZ9rneW0xiUhRnPPeTetdUn4zhbHfoywoqm2JVOVtwiZmtt/Z22liWNMy94iFOoY5bozFGptSeYl\noY+m83JevPdO29bXZ+v7KJY56ypja8OU1/Y5tzpV9/+JGo6UoUQ5xqxUVV/XY8k+6riyWlVty2pV\nXZ8Z8lXH1VXN6u4L83W9n5iqQNW1Lbn33yJcvnOe4YBb/J8HzouZOAAAAAA74CEOAAAAwA54iAMA\nAACwA4tz4nS5Hc7S1vGJZx3vuDa2c22Jy6lY27pe5v9Ym/dmq+22iH+dezzddtXU+Vtb2nVJyfQu\nZnrNZ/Ms3vdzS7Kv6eNt1uQ2qro48i1sXTa+knMH4PQdO+/cElO/E0vuV7r7oy6PS+aXmVtO+8qV\nK6P1Mi9NvZ/r8vFkW90uc9jUEuPZT46566MeW75eUmK8K02exzP3XmarHDjH/n+anDVcdmbiAAAA\nAOyAhzgAAAAAO7DbcKq9Oe+QhrllsY8d2rHk/bOmxGW3zdwQqWEYj3PuOar9r53qOTcsqyt3vTYk\na+241thquvgWoWlrzt0S5znV1zRjuBhef/9s9Zn23XC6zrr8+Fxrw8a7ktY1PKgzVY78r//6r0ev\n8/6otnWhVhnulOvVPmt58AzzylCr2keeozquLiwq+8wwsmGYH4bV2SKE6jz/X7PH/uFdmYkDAAAA\nsAMe4gAAAADsgIc4AAAAADuwOCcO63KdnEU55bl9rM2Xsia/SLW2/Hhnbk6UJeOqscpTfXZjztjk\nul7X/xZ5p7aI5V2SL2nude1i5LtrNbeU+5JxbbkNwLH5brq8Tqn8eFrz2z8Mfbnrn3766Y3rDcP4\n3mmqfPbbxph91Jw1mdum5pe5evXqZFv2eePGjcNyzfcz9zrWvDc5rjw/9XWXK3HtfRRwmszEAQAA\nANgBD3EAAAAAdkA41QrnOQ1xbihPLZuYU0ZzSmhXFrvro5ZXzO3qNNDUhcnMDWGq01i7ablzpx3X\nPvP46rHm/nKqbJ2Wm2Ppypt304W77XJ6be2n265ry2m59TrmeejKedZznn1mmczuOk6VEX1T/6YI\nA5fB6++3rcJp1oSGc3qOHabeWfseyu3qPVDee7x48WLU9vLly8Nyhi09f/58cl9dKFe9j+rGlfd7\nXYh8V0Y8jy3vh+pYahhWttV7s640eXd/nE61jDgwzUwcAAAAgB3wEAcAAABgBzzEAQAAANgBOXHO\nydoS4JnPpMbafvDBB4flK1euTG6X63388cej9TKvS40Hzj5q/pd8XeN1s61ul/HONdY2c7DkdjVW\nuMvH0m2XscOZJ2gYxrHWNcY4X8+NRc/+hmF8fer5ytfXrl0btWUMdS0zmecr953XexjG56H2kcdW\nr1X2Wc9X7rvGmP/www+H5WfPnh2WHzx4MFrvyZMnh+UaK57jWlJKFAAukiW/c8f+TVzbf/c7nvcl\nT58+HbXlvVPe23T5Hev9SpcXMu+/unyF9d4pz0PeD9U+8p635vF5/PjxYfn+/fujtly33mPlPV2X\nd3LttXJfBafJTBwAAACAHfAQBwAAAGAHhFOdkbVlHmt563xdp4jeuXPnsHzv3r1RW05BvX379mG5\nhtrk1NIuLKrK/muYV4bC1OPJfXRTNnNcdRrr1DiGYXyOuimo9fpk+FOdDpslLruS7zdv3nxjf7XP\n7K/2053zGnKUx5rnvF6PueXT63Hn69pnjrNex5winGOu04W/++67w/L3338/2Ud9X+Z1rGPeoqQm\nwEXgO4+zNPf3d0mJ9AzPzvuQ+tufqQKuX78+autCpvJes7sH6sp85/1Klfd79R4oj+3Ro0ejtrx3\n6lIKHOMz3pWR7/5v4/sGjstMHAAAAIAd8BAHAAAAYAc8xAEAAADYATlxzklXrjvLSmdelfq6tn3+\n+eeTbZn7JPPg1FjhjG+t5Q9TzceTsoRiHUvNDZPr1pw1OZYcf82Jk9vVcpF5rPWcdPG6uV2Ni85z\nluvVGOku90zGT9+9e3fUlsdX443zvN+6dWvUlsfT5a/Jc1T7z3HV0urZ54cffji573r9832Z+/7k\nk08m1/vNb34zavvqq68OyzVWPGPMzzpWXMw3cFam8k/k99Da/HtwTEt+K7vcM+nBgwdv3OZt+877\nsdp/3i/Ve/O8v6j3R3kfmvfO9Z4k74/qvcyTJ08OyzVX4twy4scmBw6cDjNxAAAAAHbAQxwAAACA\nHRBOtbGpktM1pKULi8kwkxq2ktvVkKbPPvvssJwhWcMwLk+YY6lTIzPcpYYATYU31df1WHO7OuW1\n6zPb8li7sKV6PN0U265seVdqO+UU13rcqU69zVLodbptHl8NP8vjq6FjuY/aZ8rzVY8t912PJ1/X\n0Lds66b65nr1fZ/jqqF8OQW5nstUx1Vfz7G2LPnaMAZTkIFjmfud5XuI81Lfe10YdP6m53r1Hi7v\neTNMaRjGofX1PqqT++vCpHLf9R4k77FqyFSu24WGnxLfG3B+zMQBAAAA2AEPcQAAAAB2QDjVxnJ6\ncma2r2Exd+7cOSx/+umno7aPPvrosHz79u1RW05dzJCcYRiHUNW2qSmPNYQpdSEzNXQkp4XWUJi5\nU1e7akhTYWpVdzw1DCtf131nP/U85Lq1ekHKc16PO89JdzzdmOs1zWs+9xzVKbo5hbdOT87t6lTf\nrNJV3+u5bldhIc/Rl19+OdlHle/LOm26m/48NWW7nruubW44wilVjDH9GQAA9stDHACAneoeEncP\nmj3Q5RTl+7J7z3Z/pMk/vj19+nTUln9oqn/46z4T+Yet+keuqT9W1XF1uQvn5mLc4nO75A9Lvifg\nNAmnAgAAANgBD3EAAAAAdkA41TvqSnRnWex79+6N1ss8OHfv3h21ZVnx69evj9pyCmeXj6WbWpr5\nS2qulsy5smQ6Z/ZZ8/HkOOuYc/91LFPlu2v/OebunNR8KTnFth5rvq5lIHNcXfn0PJ46rryuXZ6g\nOtU3z0Nty2swtU0dZ913vk9qnqB8XXM15XnoynrnWGq+n+4aZ56oei5zavS33347asvjq9dxKidS\nl6uny4nT2SInTu2j+3x207Ln5vExhRpOy+vP7trPphAK9m4q1KorU15Dn7IceL0H6j4jU6FcwzB9\nDzE3HKzb1zH4fMP+mYkDAAAAsAMe4gAAAADsgHCqFbpy1xkSkiW/b926NVov2zLsahjGoTZd+EYN\nk5kKpxmGcQhKTuHsynrXkKwM36n7ynVfvXo1ue8aQtNN6azn5bU6/TXPQ+0vx1LDafJ1HXNXfjx1\nbV1J9i7kqAsPy/dGvf55fXJ/+V4bhvH5q310lRNSTkcehj40LY8hr2kXwlbbMrzq448/HrVlONUn\nn3wyanvw4MFhuQune/78+TBH936t5zJf1/dJV34+X+d6XSWLak0VjTrObszA2TvLz+DcsEs4BfU9\nmr9f9beyu7eZG3K4RSjyZf5c+X6Bd2cmDgAAAMAOeIgDAAAAsAMe4gAAAADsgJw4M3R5abp8FpkL\npJaDnlu2uCu73cUA1+0yJjhzddR8GdlnV9K4K6fdlRiv56HuP+WxZv81f00ed+0/1ePJfdc+8/i6\nPD5d/HRXyjv76PISVc+ePZtcb+q9V3MB5bF2+XLqmKfyKg3D+LzX92Xm/8lzXnPIdLoy9bnvejyZ\ni6rmvcnz0sXId3lpsq0bV82BlO+bev0zb1BXuj3PZb0eeaz5nhmG8XEvOSfdd4+Ydri45pZdhlO0\n9veqvu+917fjXHLq9pC3yUwcAAAAgB3wEAcAAABgB4RTvaOuxHiGo9SS3Pm6hq1k+E4X0lRl2ETd\n31S5xbre3NLHdVwZAlK3y7LYdfy5vxoKleEjOf4ujKyGcqU65jrOlNeghnzl/rqQoNxfHVc3NS/P\nUd331PurjjlLgNdwsHz9+PHjyXHU65HHcPv27VHb3PL2uV49d3k8dcyp+8xV3eflzp07h+W8Hl2I\nX/ee6cLP6vXuwv6yny6EsQvXy5C5hw8fjtrye+LRo0ejtvv37x+W63sj31NdGCZwHK+/+07p87aH\nKeewhvczMAx9SHGnfofM7Wfud4+ZOAAAAAA74CEOAAAAwA54iAMAAACwA3LirNDFtE2VB+/KD9ec\nHtlHzT2RuS7qdpmzpO4v82dk/10uk5qDI9fttuvasuTzMIzLFtf95fF0JZm7stjZZ+btqf0/ffp0\n1Da3rHRXbrzLz9PlaunKj+d1nVvuur6HcruutHaX26jmrMn3c33vZdnvLj9SbteVde9y9XT5cur+\nclxdzqV839R95/m7cePGqC3zBnX5bLqcS5mHpr6fciy1//yeqGO+d+/eG5eHYX5+oS63lTwCcBxT\nn62zzkvjMw7A3q3NdXMq/ZuJAwAAALADHuIAAAAA7IBwqglzp0B1ZbjnhkzVMIkuDCfDN7oxzg0B\n6kJM6rFlWEkNmcmQkJcvX47a8ti7kKMsKV7HNrckd12vKwmd1yfLTQ/D+PjqmLtwpyldiel6Dboy\n4jmu+p7KEKQuZGpuW9338+fPD8tZpnoYhuHmzZtvXK7jzPHX485wpBoC1L3X8xrUMed5z/d23S7D\nlup7IUOOamn1fF3DqbrwsAcPHky25TXI81DPQa5XvzO60KcaVpi++OKLw3L9HOdnvAsBE2oBZ+us\nP3PKigOwd11KjD0wEwcAAABgBzzEAQAAANgBD3EAAAAAdkBOnBlqnNyafDk1f0mX2yTzW9Q8G5m7\no+bS6EotT+XPyJLPwzDOZ1LzpXQ5fnIsWd54GMbnofaZeVZqDo6p/Cw1Br/LUdPFO07lahmG8Xmo\nOURyHzU3SMrtas6SbKs5S/JcdrmU6rFmTqFcru+TvD5r855057nKc9vlXOry5aSuhH197+UxdHmi\nMrfNL3/5y9F6f/u3f3tYvnv37uRY6rW6devWYbnmEMq2mgsq1/36668n+8jvgu6c1HOZ571+/vPz\n+Nlnn43a8n309OnTyT6VG4fz033m9hjzDwDHcEr53db8PpuJAwAAALADHuIAAAAA7IBwqhm68J1u\n+lWGH2TowzCMwylqyNSHH354WK5hPnPDTGpoR4ZCZajQp59+Ornvuq8MtajHk21PnjwZtWXITh1X\ntnWhPbXk9JQaOpRhHvV4cupaLT+d46x9ToVh1bCYDJnK6137qGE+2U+dXpfjrMeT5y+vTw2Lytd5\n3YZhHLLThUzV8J081noup8q1dyF/XenzOq4MCaptXant3O7jjz8+LH/55Zej9X71q18dlut1zGOo\n7+3s/5NPPhm15f5qCFheuwzz+h//43+M1utC+abKzQ9DH2KYY378+PGorQsBneofOD4hVACwzN7v\nV83EAQAAANgBD3EAAAAAdsBDHAAAAIAdkBNnQpc3oouhy5wcuV4tp5w5Xmp+kcyRU/N4dLkoch81\nl07u76OPPjos//3f//1ovSwrXPOl/OEPfzgs//73vx+1daWp8/zVcWXujprPZKqPul6+ruc5t6tt\neS5rPptU8/Hk69pnyuvaldOu/ee4utL0NS9R7iP33ZWfruXTM59NPSc5ltpn937OMef+6r7zdc1D\nlNexnq/uPZT5YOrnNvvJ467l4LucEt12+Tmr1z8/I3Vceb6++uqrw3J9L+TrLldW/Tzmdt2x1eOp\nn90pp1SyES6SNZ+n7l4GADh7W/wem4kDAAAAsAMe4gAAAADsgHCqFeaGC2SIQy3zm2EfWdZ5GIbh\nwYMHk33WflKGbNSQkwxxyTCPe/fujdbLEsq1xHCGrdTwjS48KMNksoTxMIzLYtdj68J3Uheakuek\nK7XcXccajpLnIberoTwZjlTDg/IcdSXAa7nuPJ4urCz76Kbs1TCfLgQs1VLbGQJYt8vrn/134UFd\nCGNt68KKutC0PEcPHz48LNfPX35GaohZvs5y4HV/33777ajt0aNHbxzHMAzD/fv3D8sZTvXDDz+M\n1stwuhqalu+v+pnLa1DfQ1nuvI4rPz/1PM/9LAHnRylyADie7re0u1de8xtsJg4AAADADniIAwAA\nALADHuIAAAAA7ICcOBPmxrR1unwcmc+i5hDJfBM110W2deOoOWSyn8xt8d13343W+/777w/L33zz\nzagt89dUOa6axyX3XfPlZB6RWtI4z1Fej8zbMQzj3B1dWeSaxyOPp/Y51X+VOUVqzp0cSx1Xl8cl\n9/fkyZNRW75X6nZTZdi7MuVdbpNaUjqPIXPgDMP4Gtf381RJ9rpe7ru+T6bGPwzj817bch81L1Fu\nl+f5t7/97Wi9zJdT897cvXv3sFzPSR5DzXuV+aZqW75++vTpYbnmr8nrWo87X9f3b57n+pnOfdfP\nRFfKXR4cOE1zc/idav8AcGxr88LV370uh+fWzMQBAAAA2AEPcQAAAAB2QDjVCnOnR2XIQS1N3OlC\nZjK8ogt3yVCk+jqXayhHhoRkaNUwjKeILSkxnuFVdbsM0ajhLlMlp2t40NQ2w9Cfy+54MtSmC6fK\n/dXwoNyuO1/1vVGvXcp91GOtIS6v1ZC8LA9e27rtMkSvK2k9t1x7HW++f+tnLF93IXM1pC1DguqY\nc5x5DZ4/fz5aL0Of6rHle7u+73N/9b09Nf5hGF/XPOf1PTRXPSd5rPW9lt8HtSx6ljjvPhPA6RDi\nBAC9s/it3Dq8ykwcAAAAgB3wEAcAAABgB4RTTeiyS8+dctVV+ulCglLdLkNhavWYHGcNvcgQityu\nhlpkuE4N88njqdWvMlykhmh05yHVcJSpCktd2FKnnq98XfvIfdewn7r/17oKQbWPLjxsqv9hGJ/3\neg3qtXzt+vXro9cZ5lPbuupRcytjdceTY67npKv0lW31WuVYathSrlv7rOfvtfr+7cLi8j1U++tC\n5nK7rmJbHndXQa1+R+WYa1hkhkVlqNgwDMP9+/cPy1mVaxjG56W7BgAAwPF4iAMAcMlsEZ8v5w4A\n/F/HLiuehFMBAAAA7ICHOAAAAAA7IJzqHXVTibOt5uqYyvcyDOPcEzWPR65b85lkefCazyTzdWSf\ndfy570ePHo3asrRzl7el5jrJXB5dbqAur0euV0tFz83HMfecLJH7ruPvyq7PLRXe5aWpY879deXA\nP/zww8Ny5l8ZhnF57a6cdi4Pw/jYa+6WzAeT69Vjy89I3Xeeo5pfJs9t7TPXrZ/BPH+5XN+jeY3r\ney33/fTp08m2+pnIcdWS6Xn+un1nnzV/TZYKrzlx8nNdc+Lk9e/yRMmBA/vX5f4DAJZZG2K85jfY\nTBwAAACAHfAQBwAAAGAHhFOtkFOl6rSpqRCNGuaR4SG1NPHdu3cPy12p8K6tm5bVlYquIScpj6eW\nYc4+azhKhnlVXdnkGoL0WoaK1H3X0KR8XcNW6jGkPH/1ePIcZZ9duMvNmzcnx1zHkaFKdczZZx1X\nbpchVPX9ldejtmUIzZIS4N17Ko8hr0e9Vhke2IU+1fd2vq7nMkOHarhTvvdyu7rvHGc3TbILMaxt\neS7r+zz3V69B6q5VHk/9vHSlwrOfbt/AxbJkCnh+53b3GipXAXCe6m/UqfwubTEuM3EAAAAAdsBD\nHAAAAIAd8BAHAAAAYAfkxJmhxqllHFtty9eZx6Pmfsn8HDUnRuYQqTlLss8u90jNQ5N5Prp8HLm/\nmhMjj3sqX80w/GWejTz2Lp/Jkvw8KcdZr0f2Uc/lvXv3Dsu1ZHYeQx1HzVMzte/cX83/ktenK/Nd\nt6vlwqfkerUUfY6rnpM8hnrc2Wc9X3ntaq6efK9kH/X9lXlj6nuoKxX+u9/97rD81Vdfjdr+8Ic/\nHJYzP07tpyvlndaW4e3y5czVxc/W914eQz3P3XanEisMbG+r/DW+JwDYg1P9vVrybGGKmTgAAAAA\nO+AhDgAAAMAOCKdaoZvmlOFOGQLy+PHj0XoZklPLT2c4Sg1bynCUGraSoTE1JGQqbKkLHakhLV0I\nVZ6TGjrShYdlWFkN+8jjy/67MLLaR4YjvXz5cnK7GrbUncu8xrlefV9kGFMXFlXDg7rtutL0U2Ey\nXSnyOr2+Oyf5uvaZY66lvFNXtjr7rCXMv/nmm8NyhkgNwzD88Y9/PCx/++23o7Ysr137nPocLCn7\nN3f649o+527TOdWppMDZ8l0AAOdvbXqGZCYOAAAAwA54iAMAAACwAx7iAAAAAOyAnDjvqCvtm6W1\nnzx5Mlov88bUuLjMPfPZZ5/N3vdU6ethGOciyX13+V7u3r07OeZaMj37yVxAw/CXuVvSVN6bur/M\nIVNLRb948eKwXEtfZ/nu2pbHUI8nx9yVGM9rVfvI61rPQR5bl1+mtuV5rtc7x9KV/M5zmeeuqtvl\n8dRS57luzaVT+3mty89Qz+X9+/cPy7/97W8n22reo3w/d6W2u9w2W+SRWFK2fGrdJXl1AM6a7ygA\nTslefpeUGAcA+P+3dwc7TuRAGIA9Z4QQoDlx2JfY93+DfYiROCA0cEGcs6eN/i7teBLkTlKZ7zs1\n6sRtuyMMlqsKAOBO2cQBAAAAaEA41WJ5BCrDN7LU8Rjb8t01/CjDd+q9T58+Ha9riM779++P17XM\ndz4vw3BqWEz2v4ba5HhqyEyGANWja/nZWk49n1fbzJCa/FwNI8qx1XH//v37eF3Hk/NX5yFDk+p4\nch4yVKi2kX+u7yrbnJW+rvdSDR3K300+u4YR5efqnM9KxWe/ZmFY9Tf7Ukn22v6PHz+O11k2vP65\nhibm8+p85dj3CJO6ZBu3egQU6GFFSVMA6Oqe1kEncQAAAAAasIkDAAAA0IBNHAAAAIAG5MTZ0Uv5\nccbY5jOpJawzf07mCRljW/b7y5cvm3tZevvz58+be5k3JvuVJbjH2OabqXlPMofJu3fvNvdyfDVX\nS6r5UnKsszwr2edfv35t7mX+mir7Vd9BqiWtc6x1jrIv+b2ajyXncvbs+r3M41Pvffjw4Xg9K92e\nc1RLq2c8aO3X7D3mPNc5yb48Pj5u7uV7zd/69+/fN5/79u3b8frp6Wlz7+vXr8fr+v7ztz0rIz7L\nKSPfDJ11KaHJPm4lxt/vDoBrm62Jp66Xe69nK9p3EgcAAACgAZs4AAAAAA08nHOc5+HhwVnZC8ij\nXrUMc4a01DCZLDGeJb/H2JaSzvZrye+PHz+e1Mdamjr7kuEtY2xLU9d7GbJTQ4dyDPm8LOs9xjxk\nJsN36vdybmtIW3629jnLvGe/ahs5zzUsLsORamnyDDnLuRtj+/5n4VTZrzonOZ4aijYr1z17XoYx\n1e/l85+fn4/XdU5+/vx5vK6hdbOQqTpHp3L0f3+rwnxmx1+9x30cDofbiNFhjDH/95dwKgDeqroG\nrl4TV6VjyH7V75V7/xwOh79fa89JHAAAAIAGbOIAAAAANGATBwAAAKABJcZvwDn5HjLvyqw0+Sw+\nMEtfV7O8J9lGzUOSOXFqTpTMuzLLG1Nzz+TYM1dPLVM+y5eSuW1mOXFqOe38bJ2vHHs+r/Y/26i5\njXJsWVK8qu+jjuEl2ZeaO2kWv5nPm/2+ak6k7Fcdz0u/qVnem/r7yvmr/Z+NZ0Vc7CyG9dTvVdnO\nirwxe5SYPmfuXnreqhwZq3NtrBjbOc+QK4Q9nfp34CwGfwW5qwC4hFxvZv/nfSX3zE69+3+rcuv8\nx0kcAAAAgAZs4gAAAAA0cG441fMY42mPjrxlq45XnVpquYYjdZDlp3l7rllG/E/bOPV71+zjpdu8\nFZcYW/P5++vaHWCNU/99cStlygHgNaeuX3uvbXukMzjVWZs4h8Phca+OAAAAAPAy4VQAAAAADdjE\nAQAAAGhAiXEAgDt3zbw3yo8DsIe915Bz8t5csoS5kzgAAAAADdjEAQAAAGhAOBUAwB24RMjUS0fE\nlSkH4NpWhDGtCIvau/y4kzgAAAAADdjEAQAAAGhAOBUAQFMrwphWHPM+tWLHqucB8DbtHb77p2vU\nJdc2J3EAAAAAGrCJAwAAANCATRwAAACABuTEAQC4A7eaa+ZW+wXA/Zrlztk7F9zenMQBAAAAaMAm\nDgAAAEADwqkAAJoSqgQAr1u9Xu4drjXjJA4AAABAAzZxAAAAABqwiQMAAADQgJw4AAAAwN2oeWky\nh83eOWtqvpzVz3MSBwAAAKABmzgAAAAADQinAgBgN9cswwoAY9zXeuMkDgAAAEADNnEAAAAAGrCJ\nAwAAANCAnDgASwNqtAAAATFJREFUACw1y4MDALcqc+fc6lrmJA4AAABAAzZxAAAAABoQTgUAwMXc\nU5lXAC5rtoasDn+qz8r2r7mWOYkDAAAA0IBNHAAAAIAGbOIAAAAANCAnDgAAZ5vlHpD3BoBLO3Xt\nWZE7p7ZxyXXPSRwAAACABmziAAAAADQgnAoAgLOtODp+zePoALxNf7rW5PdWlzM/h5M4AAAAAA3Y\nxAEAAABowCYOAAAAQANy4gAAvDEZy793Hhp5bwBgHSdxAAAAABqwiQMAAADQgHAqAIDb9jzGeFrZ\n4CVDmmbPEloFQEc7rV9/nfKhB4snAAAAwO0TTgUAAADQgE0cAAAAgAZs4gAAAAA0YBMHAAAAoAGb\nOAAAAAAN2MQBAAAAaMAmDgAAAEADNnEAAAAAGrCJAwAAANDAv06yh74vgmCvAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fda9dfe1e80>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# MLEM(20) TOMOGRAPHIC RECONSTRUCTION\n",
      "\n",
      "# Setting up the plot surface\n",
      "fig = plt.figure(figsize=(20, 10))\n",
      "gs = GridSpec(nrows=2, ncols=2)\n",
      "# First axes\n",
      "ax0 = fig.add_subplot(gs[0, 0])\n",
      "ax0.imshow(mlemvolume[0,0,:,61,:],cmap='gray')\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "# Second axes\n",
      "ax1 = fig.add_subplot(gs[1, 0])\n",
      "ax1.imshow(mlemvolume[0,0,:,:,62],cmap='gray')\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "# Third axes\n",
      "ax2 = fig.add_subplot(gs[:, 1])\n",
      "ax2.imshow(mlemvolume[0,0,23,:,:],cmap='gray')\n",
      "cur_axes = plt.gca()\n",
      "cur_axes.axes.get_xaxis().set_visible(False)\n",
      "cur_axes.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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93p/M65HHo9ed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\nAOzA/wBjL/OvDAp4ggAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fda9c59d630>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# EVALUATE RESULTS WITH FIGURES OF MERIT\n",
      "\n",
      "test_images_norm=np.copy(gt.squeeze())\n",
      "mlem_10_norm=np.copy(reco_data20.numpy().squeeze())\n",
      "denoised_norm=np.copy(test_res_denoise_data.squeeze())\n",
      "denoised_norm2=np.copy(test_res_denoise_data_hybrid.squeeze())\n",
      "denoised_norm3=np.copy(test_res_denoise_data_ellips.squeeze())\n",
      "\n",
      "\n",
      "index=[]\n",
      "for i in range(test_images_norm.shape[0]):\n",
      "    if np.amax(test_images_norm[i,:,:].flatten())!=0:\n",
      "        index.append(i)\n",
      "\n",
      "\n",
      "mlem_ssim_list = []\n",
      "ellipses_ssim_list = []\n",
      "ellipses_ssim_list2 = []\n",
      "ellipses_ssim_list3 = []\n",
      "mlem_psnr_list = []\n",
      "ellipses_psnr_list = []\n",
      "ellipses_psnr_list2 = []\n",
      "ellipses_psnr_list3 = []\n",
      "\n",
      "for i in range(0,35): \n",
      "    mlem_ssim_list.append(fom.ssim(mlem_10_norm[i,:,:],test_images_norm[i,:,:]))\n",
      "    ellipses_ssim_list.append(fom.ssim(denoised_norm[i,:,:],test_images_norm[i,:,:]))\n",
      "    ellipses_ssim_list2.append(fom.ssim(denoised_norm2[i,:,:],test_images_norm[i,:,:]))\n",
      "    ellipses_ssim_list3.append(fom.ssim(denoised_norm3[i,:,:],test_images_norm[i,:,:]))\n",
      "    \n",
      "    mlem_psnr_list.append(peak_SNR(mlem_10_norm[i,:,:],test_images_norm[i,:,:]))\n",
      "    ellipses_psnr_list.append(peak_SNR(denoised_norm[i,:,:],test_images_norm[i,:,:]))\n",
      "    ellipses_psnr_list2.append(peak_SNR(denoised_norm2[i,:,:],test_images_norm[i,:,:]))\n",
      "    ellipses_psnr_list3.append(peak_SNR(denoised_norm3[i,:,:],test_images_norm[i,:,:]))\n",
      "\n",
      "\n",
      "mlem_ssim=np.mean(mlem_ssim_list) \n",
      "ellipses_ssim=np.mean(ellipses_ssim_list) \n",
      "ellipses_ssim2=np.mean(ellipses_ssim_list2)\n",
      "ellipses_ssim3=np.mean(ellipses_ssim_list3)\n",
      "\n",
      "mlem_psnr= np.mean(mlem_psnr_list) \n",
      "ellipses_psnr=np.mean(ellipses_psnr_list) \n",
      "ellipses_psnr2=np.mean(ellipses_psnr_list2)\n",
      "ellipses_psnr3=np.mean(ellipses_psnr_list3)\n",
      "\n",
      "loss = nn.MSELoss()\n",
      "loss_mlem =loss(reco_data20,gt.float())\n",
      "loss_only =loss(torch.tensor(test_res_denoise_data),gt)\n",
      "loss_hybrid =loss(torch.tensor(test_res_denoise_data_hybrid),gt)\n",
      "loss_ellips =loss(torch.tensor(test_res_denoise_data_ellips),gt)\n",
      "\n",
      "print('\\nAverage SSIM for MLEM: {}\\n'.format(mlem_ssim))\n",
      "\n",
      "print('\\nAverage SSIM for denoised images with ellipsoids network: {}\\n'.format(ellipses_ssim3))\n",
      "print('\\nDSSIM: {}'.format(ellipses_ssim3-mlem_ssim))\n",
      "\n",
      "print('\\nAverage SSIM for denoised images with miniPET network: {}\\n'.format(ellipses_ssim))\n",
      "print('\\nDSSIM: {}'.format(ellipses_ssim-mlem_ssim))\n",
      "\n",
      "print('\\nAverage SSIM for denoised images with hybrid network: {}\\n'.format(ellipses_ssim2))\n",
      "print('\\nDSSIM: {}'.format(ellipses_ssim2-mlem_ssim))\n",
      "\n",
      "print('\\nAverage PSNR for MLEM: {}\\n'.format(mlem_psnr))\n",
      "\n",
      "print('\\nAverage PSNR for denoised images with ellipsoids network: {}\\n'.format(ellipses_psnr3))\n",
      "print('\\nDPSNR: {}'.format(ellipses_psnr3-mlem_psnr))\n",
      "\n",
      "print('\\nAverage PSNR for denoised images with miniPET network: {}\\n'.format(ellipses_psnr))\n",
      "print('\\nDPSNR: {}'.format(ellipses_psnr-mlem_psnr))\n",
      "\n",
      "print('\\nAverage PSNR for denoised images with hybrid network: {}\\n'.format(ellipses_psnr2))\n",
      "print('\\nDPSNR: {}'.format(ellipses_psnr2-mlem_psnr))\n",
      "\n",
      "print('\\nAverage loss for MLEM: {}\\n'.format(loss_mlem))\n",
      "\n",
      "print('\\nAverage loss for denoised images with ellipsoids network: {}\\n'.format(loss_ellips))\n",
      "print('\\nDLoss: {}'.format(loss_ellips-loss_mlem))\n",
      "\n",
      "print('\\nAverage loss for denoised images with miniPET network: {}\\n'.format(loss_only))\n",
      "print('\\nDLoss: {}'.format(loss_only-loss_mlem))\n",
      "\n",
      "print('\\nAverage loss for denoised images with hybrid network: {}\\n'.format(loss_hybrid))\n",
      "print('\\nDLoss: {}'.format(loss_hybrid-loss_mlem))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Average SSIM for MLEM: 0.7873310701136185\n",
        "\n",
        "\n",
        "Average SSIM for denoised images with ellipsoids network: 0.6290718727652156\n",
        "\n",
        "\n",
        "DSSIM: -0.15825919734840288\n",
        "\n",
        "Average SSIM for denoised images with miniPET network: 0.6567582893771549\n",
        "\n",
        "\n",
        "DSSIM: -0.1305727807364636\n",
        "\n",
        "Average SSIM for denoised images with hybrid network: 0.5972113236933702\n",
        "\n",
        "\n",
        "DSSIM: -0.19011974642024831\n",
        "\n",
        "Average PSNR for MLEM: 22.613935470581055\n",
        "\n",
        "\n",
        "Average PSNR for denoised images with ellipsoids network: 22.746450424194336\n",
        "\n",
        "\n",
        "DPSNR: 0.13251495361328125\n",
        "\n",
        "Average PSNR for denoised images with miniPET network: 24.240398406982422\n",
        "\n",
        "\n",
        "DPSNR: 1.6264629364013672\n",
        "\n",
        "Average PSNR for denoised images with hybrid network: 25.200138092041016\n",
        "\n",
        "\n",
        "DPSNR: 2.586202621459961\n",
        "\n",
        "Average loss for MLEM: 0.0010697725228965282\n",
        "\n",
        "\n",
        "Average loss for denoised images with ellipsoids network: 0.0009131521234638625\n",
        "\n",
        "\n",
        "DLoss: -0.00015662039943266576\n",
        "\n",
        "Average loss for denoised images with miniPET network: 0.0032371377263845577\n",
        "\n",
        "\n",
        "DLoss: 0.0021673652034880295\n",
        "\n",
        "Average loss for denoised images with hybrid network: 0.0008794524687601798\n",
        "\n",
        "\n",
        "DLoss: -0.0001903200541363484\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    }
   ],
   "metadata": {}
  }
 ]
}
